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: "exploring the limitations of behavior cloning for autonomous driving"
  Abstract: Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, including in unseen environments, executing complex lateral and longitudinal maneuvers without these reactions being explicitly programmed. However, we confirm well-known limitations (due to dataset bias and overfitting), new generalization issues (due to dynamic objects and the lack of a causal model), and training instability requiring further research before behavior cloning can graduate to real-world driving. The code of the studied behavior cloning approaches can be found at this https URL .

Connected through hyperedge HE-1 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies"):
  with members:
    - "adaptive stress testing for autonomous vehicles"
    - "a survey of deep learning applications to autonomous vehicle control"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "adversarially robust generalization requires more data"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
    - "reward estimation for variance reduction in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "who make drivers stop towards driver centric risk assessment risk object identification via causal inference"):
  with members:
    - "urban driving with conditional imitation learning"
    - "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"
    - "causal confusion in imitation learning"
    - "grounding human to vehicle advice for self driving vehicles"
    - "goal oriented object importance estimation in on road driving videos"
    - "causal induction from visual observations for goal directed tasks"
    - "a self validation network for object level human attention estimation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-3 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "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"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-4 (induced by source paper "preventing imitation learning with adversarial policy ensembles"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "policy poisoning in batch reinforcement learning and control"
    - "learning novel policies for tasks"
    - "causal confusion in imitation learning"
    - "risk aware active inverse reinforcement learning"
    - "interpretable latent spaces for learning from demonstration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-5 (induced by source paper "end to end model free reinforcement learning for urban driving using implicit affordances"):
  with members:
    - "is deep reinforcement learning really superhuman on atari leveling the playing field"
    - "learning by cheating"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "end to end race driving with deep reinforcement learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "end to end vehicle lateral control using a single fisheye camera"

Connected through hyperedge HE-6 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"
    - "learning robotic assembly from cad"

Connected through hyperedge HE-7 (induced by source paper "learning by cheating"):
  with members:
    - "does computer vision matter for action"
    - "monocular plan view networks for autonomous driving"
    - "objects as points"
    - "pytorch an imperative style high performance deep learning library"
    - "an algorithmic perspective on imitation learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-8 (induced by source paper "urban driving with conditional imitation learning"):
  with members:
    - "causal confusion in imitation learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deep imitative models for flexible inference planning and control"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"

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.CV(Computer Vision and Pattern Recognition)