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 "online planning for decentralized stochastic control with partial history sharing"):
  Abstract: In decentralized stochastic control, standard approaches for sequential decision-making, e.g. dynamic programming, quickly become intractable due to the need to maintain a complex information state. Computational challenges are further compounded if agents do not possess complete model knowledge. In this paper, we take advantage of the fact that in many problems agents share some common information, or history, termed partial history sharing. Under this information structure the policy search space is greatly reduced. We propose a provably convergent, online tree-search based algorithm that does not require a closed-form model or explicit communication among agents. Interestingly, our algorithm can be viewed as a generalization of several existing heuristic solvers for decentralized partially observable Markov decision processes. To demonstrate the applicability of the model, we propose a novel collaborative intrusion response model, where multiple agents (defenders) possessing asymmetric information aim to collaboratively defend a computer network. Numerical results demonstrate the performance of our algorithm.

Includes members:
  Member "finite sample analyses for fully decentralized multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "policy optimization provably converges to nash equilibria in zero sum linear quadratic games")
  Member "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a theoretical analysis of deep q learning")
  Member "fully decentralized multi agent reinforcement learning with networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cluster based social reinforcement learning")
  Member "decentralized stochastic control with partial history sharing a common information approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")
  Member "improved memory bounded dynamic programming for decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on solving cooperative marl problems with a few good experiences")
  Member "maa a heuristic search algorithm for solving decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep actor critic reinforcement learning framework for dynamic multichannel access")
  Member "optimal control strategies in delayed sharing information structures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mimo amplify and forward precoding for networked control systems")
  Member "policy iteration for decentralized control of markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies")

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)