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: "query efficient imitation learning for end to end autonomous driving"
  Abstract: One way to approach end-to-end autonomous driving is to learn a policy function that maps from a sensory input, such as an image frame from a front-facing camera, to a driving action, by imitating an expert driver, or a reference policy. This can be done by supervised learning, where a policy function is tuned to minimize the difference between the predicted and ground-truth actions. A policy function trained in this way however is known to suffer from unexpected behaviours due to the mismatch between the states reachable by the reference policy and trained policy functions. More advanced algorithms for imitation learning, such as DAgger, addresses this issue by iteratively collecting training examples from both reference and trained policies. These algorithms often requires a large number of queries to a reference policy, which is undesirable as the reference policy is often expensive. In this paper, we propose an extension of the DAgger, called SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous driving. We evaluate the proposed SafeDAgger in a car racing simulator and show that it indeed requires less queries to a reference policy. We observe a significant speed up in convergence, which we conjecture to be due to the effect of automated curriculum learning.

Connected through hyperedge HE-1 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "reinforcement learning from imperfect demonstrations"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "oil observational imitation learning"

Connected through hyperedge HE-3 (induced by source paper "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "towards characterizing divergence in deep q learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "reinforcement and imitation learning for diverse visuomotor skills"

Connected through hyperedge HE-4 (induced by source paper "simitate a hybrid imitation learning benchmark"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "dart noise injection for robust imitation learning"
    - "human action localization with sparse spatial supervision"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-5 (induced by source paper "on memory mechanism in multi agent reinforcement learning"):
  with members:
    - "r maddpg for partially observable environments and limited communication"
    - "on the pitfalls of measuring emergent communication"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "machine theory of mind"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-6 (induced by source paper "attacking vision based perception in end to end autonomous driving models"):
  with members:
    - "key ingredients of self driving cars"
    - "requirements driven test generation for autonomous vehicles with machine learning components"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"
    - "semantic adversarial deep learning"
    - "baidu apollo em motion planner"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"

Connected through hyperedge HE-7 (induced by source paper "model predictive policy learning with uncertainty regularization for driving in dense traffic"):
  with members:
    - "stochastic video generation with a learned prior"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "stochastic variational video prediction"
    - "learning model based planning from scratch"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning a controller fusion network by online trajectory filtering for vision based uav racing"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "teaching uavs to race end to end regression of agile controls in simulation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "agile autonomous driving using end to end deep imitation learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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.LG(Machine Learning)