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 "optimistic proximal policy optimization"):
  Abstract: Reinforcement Learning, a machine learning framework for training an autonomous agent based on rewards, has shown outstanding results in various domains. However, it is known that learning a good policy is difficult in a domain where rewards are rare. We propose a method, optimistic proximal policy optimization (OPPO) to alleviate this difficulty. OPPO considers the uncertainty of the estimated total return and optimistically evaluates the policy based on that amount. We show that OPPO outperforms the existing methods in a tabular task.

Includes members:
  Member "go explore a new approach for hard exploration problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "exploration by random network distillation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "trust region policy optimization of pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical reinforcement learning with advantage based auxiliary rewards")
  Member "large scale study of curiosity driven learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "the uncertainty bellman equation and exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "count based exploration with neural density models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "curiosity driven exploration by self supervised prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "revisiting the arcade learning environment evaluation protocols and open problems for general agents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")

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)