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: "learning preferences for manipulation tasks from online coactive feedback"
  Abstract: We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. #R##N#We implement our algorithm on two high degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings -- household chores and grocery store checkout -- and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).\footnote{Parts of this work has been published at NIPS and ISRR conferences~\citep{Jain13,Jain13b}. This journal submission presents a consistent full paper, and also includes the proof of regret bounds, more details of the robotic system, and a thorough related work.}

Connected through hyperedge HE-1 (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"
    - "an algorithmic perspective on imitation 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"

Connected through hyperedge HE-2 (induced by source paper "reward rational implicit choice a unifying formalism for reward learning"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "preferences implicit in the state of the world"
    - "active inverse reward design"
    - "inverse reward design"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "the off switch game"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-3 (induced by source paper "socially aware robot planning via bandit human feedback"):
  with members:
    - "socially aware motion planning with deep reinforcement learning"
    - "modeling cooperative navigation in dense human crowds"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"

Connected through hyperedge HE-5 (induced by source paper "learning reward functions by integrating human demonstrations and preferences"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "simplifying reward design through divide and conquer"
    - "reward learning from human preferences and demonstrations in atari"
    - "batch active preference based learning of reward functions"
    - "inverse reward design"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "using causal analysis to learn specifications from task demonstrations"):
  with members:
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "counterfactual multi agent policy gradients"
    - "unsupervised learning of disentangled representations from video"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "learning trajectory preferences for manipulators via iterative improvement"

Connected through hyperedge HE-7 (induced by source paper "literal or pedagogic human analyzing human model misspecification in objective learning"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "generalizing the theory of cooperative inference"
    - "optimal cooperative inference"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-8 (induced by source paper "preference based learning for exoskeleton gait optimization"):
  with members:
    - "dueling posterior sampling for preference based reinforcement learning"
    - "feedback control of an exoskeleton for paraplegics toward robustly stable hands free dynamic walking"
    - "multi dueling bandits with dependent arms"
    - "concrete problems in ai safety"
    - "online structured prediction via coactive learning"

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)