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 "should one minimize the expected bellman residual or maximize the mean value"):
  Abstract: We study reinforcement learning from an optimization perspective. We consider maximizing the mean value (the predominant approach in policy search) and minimizing the expected Bellman residual (the Bellman residual being prevalent in approximate dynamic programming). For doing so, we introduce a new approach that consists in minimizing the mean residual $\nu(T_* v_\pi - v_\pi)$ for a class of parameterized policies. We prove that this method enjoys a performance bound that is better than the sole known bound for maximizing the mean value and that matches the best known bounds in approximate dynamic programming. We also conduct experiments on randomly generated generic Markov decision processes to compare both approaches empirically. It appears that maximizing the mean value is much more efficient, and that the Bellman residual is actually not a such good proxy for optimizing a value function. This suggests to envision maximizing the mean value for designing new reinforcement learning approaches, and that much remains to be done regarding its theoretical analysis.

Includes members:
  Member "trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "approximate policy iteration schemes a comparison":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction")

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)