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 "policy search any local optimum enjoys a global performance guarantee"):
  Abstract: Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a parameterized policy space in order to maximize the associated value function averaged over some predefined distribution. It is probably commonly believed that the best one can hope in general from such an approach is to get a local optimum of this criterion. In this article, we show the following surprising result: any (approximate) local optimum enjoys a global performance guarantee. We compare this guarantee with the one that is satisfied by Direct Policy Iteration, an approximate dynamic programming algorithm that does some form of Policy Search: if the approximation error of Local Policy Search may generally be bigger (because local search requires to consider a space of stochastic policies), we argue that the concentrability coefficient that appears in the performance bound is much nicer. Finally, we discuss several practical and theoretical consequences of our analysis.

Includes members:
  Member "on the use of non stationary policies for stationary infinite horizon markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "q star approximation schemes for batch reinforcement learning a theoretical comparison")
  Member "approximate modified policy iteration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "challenges of real world reinforcement learning")
  Member "approximate policy iteration with a policy language bias solving relational markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized neural policies for relational mdps")

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)