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 selected review on reinforcement learning based control for autonomous underwater vehicles"):
  Abstract: Recently, reinforcement learning (RL) has been extensively studied and achieved promising results in a wide range of control tasks. Meanwhile, autonomous underwater vehicle (AUV) is an important tool for executing complex and challenging underwater tasks. The advances in RL offers ample opportunities for developing intelligent AUVs. This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking. To this end, we first present a concise introduction to the RL based control framework. Then, we provide an overview of RL methods for AUVs control problems, where the main challenges and recent progresses are discussed. Finally, two representative cases of RL-based controllers are given in detail for the model-free RL methods on AUVs.

Includes members:
  Member "multi pseudo q learning based deterministic policy gradient for tracking control of autonomous underwater vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized anderson acceleration for off policy deep reinforcement learning")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "model based value estimation for efficient model free reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "synthesizing neural network controllers with probabilistic model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised object level deep reinforcement learning")
  Member "thinking fast and slow with deep learning and tree search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "prioritized experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)