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 "a deep reinforcement learning based driving policy for autonomous road vehicles"):
  Abstract: In this work, the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of the authors’ knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles’ behaviour on the overall traffic flow are presented.

Includes members:
  Member "elements of effective deep reinforcement learning towards tactical driving decision making":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "social attention for autonomous decision making in dense traffic")
  Member "combining neural networks and tree search for task and motion planning in challenging environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "task motion planning for safe and efficient urban driving")
  Member "explaining how a deep neural network trained with end to end learning steers a car":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "limits of end to end learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multipurpose intelligent process automation via conversational assistant")
  Member "on a formal model of safe and scalable self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies")
  Member "towards full automated drive in urban environments a demonstration in gomentum station california":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "long term prediction of vehicle behavior using short term uncertainty aware trajectories and high definition maps")
  Member "brain inspired cognitive model with attention for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "safe multi agent reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.RO(Robotics)