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 "q learning for mean field controls"):
  Abstract: Multi-agent reinforcement learning (MARL) has been applied to many challenging problems including two-team computer games, autonomous drivings, and real-time biddings. Despite the empirical success, there is a conspicuous absence of theoretical study of different MARL algorithms: this is mainly due to the curse of dimensionality caused by the exponential growth of the joint state-action space as the number of agents increases. Mean-field controls (MFC) with infinitely many agents and deterministic flows, meanwhile, provide good approximations to $N$-agent collaborative games in terms of both game values and optimal strategies. In this paper, we study the collaborative MARL under an MFC approximation framework: we develop a model-free kernel-based Q-learning algorithm (CDD-Q) and show that its convergence rate and sample complexity are independent of the number of agents. Our empirical studies on MFC examples demonstrate strong performances of CDD-Q. Moreover, the CDD-Q algorithm can be applied to a general class of Markov decision problems (MDPs) with deterministic dynamics and continuous state-action space.

Includes members:
  Member "multi agent reinforcement learning a selective overview of theories and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "learning to control in metric space with optimal regret":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "natural actor critic converges globally for hierarchical linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a general framework for learning mean field games")
  Member "model based reinforcement learning for atari":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "an algorithmic perspective on imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "q learning with nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "option discovery in the absence of rewards with manifold analysis")
  Member "safe multi agent reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "practical kernel based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation")

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)