human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "trust region policy optimization of pomdps"
  Abstract: We propose Generalized Trust Region Policy Optimization (GTRPO), a Reinforcement Learning algorithm for TRPO of Partially Observable Markov Decision Processes (POMDP). While the principle of policy gradient methods does not require any model assumption, previous studies of more sophisticated policy gradient methods are mainly limited to MDPs. Many real-world decision-making tasks, however, are inherently non-Markovian, i.e., only an incomplete representation of the environment is observable. Moreover, most of the advanced policy gradient methods are designed for infinite horizon MDPs. Our proposed algorithm, GTRPO, is a policy gradient method for continuous episodic POMDPs. We prove that its policy updates monotonically improve the expected cumulative return. We empirically study GTRPO on many RoboSchool environments, an extension to the MuJoCo environments, and provide insights into its empirical behavior.

Connected through hyperedge HE-1 (induced by source paper "hierarchical reinforcement learning with advantage based auxiliary rewards"):
  with members:
    - "hierarchical policy learning is sensitive to goal space design"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "diversity is all you need learning skills without a reward function"
    - "context aware policy reuse"
    - "learning multi level hierarchies with hindsight"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "optimistic proximal policy optimization"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "the uncertainty bellman equation and exploration"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "proximal policy optimization algorithms"

Question: Please tell me which class the center node 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)