human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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 .

Includes members:
  Member "causal confusion in imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "a modern take on the bias variance tradeoff in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "cirl controllable imitative reinforcement learning for vision based self driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal end to end autonomous driving")
  Member "conditional affordance learning for driving in urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "chauffeurnet learning to drive by imitating the best and synthesizing the worst":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "rethinking self driving multi task knowledge for better generalization and accident explanation ability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multimodal end to end autonomous driving")
  Member "driving policy transfer via modularity and abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to move with affordance maps")
  Member "on offline evaluation of vision based driving models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of deep learning applications to autonomous vehicle control")

Question: Please tell me which class the source paper 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)