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 "cooperation aware reinforcement learning for merging in dense traffic"):
  Abstract: Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.

Includes members:
  Member "interaction aware decision making with adaptive strategies under merging scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "driving in dense traffic with model free reinforcement learning")
  Member "safe reinforcement learning with scene decomposition for navigating complex urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "collaborative planning for mixed autonomy lane merging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online risk bounded motion planning for autonomous vehicles in dynamic environments")
  Member "hierarchical game theoretic planning for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sequential forecasting of 100 000 points")
  Member "multimodal probabilistic model based planning for human robot interaction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "prioritized experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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.AI(Artificial Intelligence)