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 "brpo batch residual policy optimization"):
  Abstract: In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e.g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is the same at each state. This can cause batch RL to be overly conservative, unable to exploit large policy changes at frequently-visited, high-confidence states without risking poor performance at sparsely-visited states. To remedy this, we propose residual policies, where the allowable deviation of the learned policy is state-action-dependent. We derive a new for RL method, BRPO, which learns both the policy and allowable deviation that jointly maximize a lower bound on policy performance. We show that BRPO achieves the state-of-the-art performance in a number of tasks.

Includes members:
  Member "benchmarking batch deep reinforcement learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an optimistic perspective on offline reinforcement learning")
  Member "multi agent manipulation via locomotion using hierarchical sim2real":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "way off policy batch deep reinforcement learning of implicit human preferences in dialog":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "residual policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "elaborating on learned demonstrations with temporal logic specifications")
  Member "natural gradient deep q learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "direction concentration learning enhancing congruency in machine learning")
  Member "off policy deep reinforcement learning without exploration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "horizon facebook s open source applied reinforcement learning platform":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "residual reinforcement learning for robot control":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning contact rich manipulation tasks with rigid position controlled robots learning to force 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.LG(Machine Learning)