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 "a general framework for learning mean field games"):
  Abstract: This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the existence of a unique Nash Equilibrium to this GMFG, and demonstrates that naively combining Q-learning with the fixed-point approach in classical MFGs yields unstable algorithms. It then proposes value-based and policy-based reinforcement learning algorithms (GMF-P and GMF-P respectively) with smoothed policies, with analysis of convergence property and computational complexity. The experiments on repeated Ad auction problems demonstrate that GMF-V-Q, a specific GMF-V algorithm based on Q-learning, is efficient and robust in terms of convergence and learning accuracy. Moreover, its performance is superior in convergence, stability, and learning ability, when compared with existing algorithms for multi-agent reinforcement learning.

Includes members:
  Member "q learning for mean field controls":
    also connected through other hyperedges:
      (none)
  Member "adaptive trust region policy optimization global convergence and faster rates for regularized mdps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "natural actor critic converges globally for hierarchical linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "q learning for mean field controls")
  Member "a theory of regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "multi agent reinforcement learning a report on challenges and approaches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time tree search with pessimistic scenarios")
  Member "real time bidding by reinforcement learning in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "jointly learning to recommend and advertise")
  Member "reinforcement learning with deep energy based policies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "a unified view of entropy regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning via fenchel rockafellar duality")

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)