human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an algorithmic perspective on imitation learning"
  Abstract: As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation. We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning. We pay particular attention to the intimate connection between imitation learning approaches and those of structured prediction Daume III et al. [2009]. To structure this discussion, we categorize imitation learning techniques based on the following key criteria which drive algorithmic decisions:#R##N##R##N#1) The structure of the policy space. Is the learned policy a time-index trajectory (trajectory learning), a mapping from observations to actions (so called behavioral cloning [Bain and Sammut, 1996]), or the result of a complex optimization or planning problem at each execution as is common in inverse optimal control methods [Kalman, 1964, Moylan and Anderson, 1973].#R##N##R##N#2) The information available during training and testing. In particular, is the learning algorithm privy to the full state that the teacher possess? Is the learner able to interact with the teacher and gather corrections or more data? Does the learner have a (typically a priori) model of the system with which it interacts? Does the learner have access to the reward (cost) function that the teacher is attempting to optimize?#R##N##R##N#3) The notion of success. Different algorithmic approaches provide varying guarantees on the resulting learned behavior. These guarantees range from weaker (e.g., measuring disagreement with the agent’s decision) to stronger (e.g., providing guarantees on the performance of the learner with respect to a true cost function, either known or unknown). We organize our work by paying particular attention to distinction (1): dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control or inverse reinforcement learning [Russell, 1998]). In the latter case, behavior arises as the result of an optimization problem solved for each new instance that the learner faces. In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples—such as robots that play table tennis [Kober and Peters, 2009], programs that play the game of Go [Silver et al., 2016], and systems that understand natural language [Wen et al., 2015]— illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions for machine learning.

Connected through hyperedge HE-1 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "monet unsupervised scene decomposition and representation"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "state representation learning for control an overview"

Connected through hyperedge HE-2 (induced by source paper "vision based robot manipulation learning via human demonstrations"):
  with members:
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "learning actions from human demonstration video for robotic manipulation"
    - "one shot observation learning using visual activity features"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "intel realsense stereoscopic depth cameras"

Connected through hyperedge HE-3 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "autonomous interpretation of demonstrations for modification of dynamical movement primitives"
    - "risk aware active inverse reinforcement learning"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "including uncertainty when learning from human corrections"
    - "learning under misspecified objective spaces"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "trajectory deformations from physical human robot interaction"

Connected through hyperedge HE-4 (induced by source paper "multimodal trajectory optimization for motion planning"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "learning novel policies for tasks"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "divide and conquer reinforcement learning"
    - "kernelized movement primitives"

Connected through hyperedge HE-5 (induced by source paper "metric based imitation learning between two dissimilar anthropomorphic robotic arms"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "model free imitation learning with policy optimization"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "tensorflow a system for large scale machine learning"
    - "learning monocular reactive uav control in cluttered natural environments"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-6 (induced by source paper "q learning for mean field controls"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "learning to control in metric space with optimal regret"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "model based reinforcement learning for atari"
    - "q learning with nearest neighbors"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "practical kernel based reinforcement learning"
    - "efficient reinforcement learning in deterministic systems with value function generalization"

Connected through hyperedge HE-7 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-8 (induced by source paper "learning self correctable policies and value functions from demonstrations with negative sampling"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"
    - "graph networks as learnable physics engines for inference and control"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.RO(Robotics)