human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "finite sample analyses for fully decentralized multi agent reinforcement learning"
  Abstract: Despite the increasing interest in multi-agent reinforcement learning (MARL) in the community, understanding its theoretical foundation has long been recognized as a challenging problem. In this work, we make an attempt towards addressing this problem, by providing finite-sample analyses for fully decentralized MARL. Specifically, we consider two fully decentralized MARL settings, where teams of agents are connected by time-varying communication networks, and either collaborate or compete in a zero-sum game, without the absence of any central controller. These settings cover many conventional MARL settings in the literature. For both settings, we develop batch MARL algorithms that can be implemented in a fully decentralized fashion, and quantify the finite-sample errors of the estimated action-value functions. Our error analyses characterize how the function class, the number of samples within each iteration, and the number of iterations determine the statistical accuracy of the proposed algorithms. Our results, compared to the finite-sample bounds for single-agent RL, identify the involvement of additional error terms caused by decentralized computation, which is inherent in our decentralized MARL setting. To our knowledge, our work appears to be the first finite-sample analyses for MARL, which sheds light on understanding both the sample and computational efficiency of MARL algorithms.

Connected through hyperedge HE-1 (induced by source paper "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"):
  with members:
    - "on gradient based learning in continuous games"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "a theoretical analysis of deep q learning"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "local saddle point optimization a curvature exploitation approach"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"

Connected through hyperedge HE-2 (induced by source paper "optimization for reinforcement learning from single agent to cooperative agents"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "addressing function approximation error in actor critic methods"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-3 (induced by source paper "non cooperative inverse reinforcement learning"):
  with members:
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "cooperative inverse reinforcement learning"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-4 (induced by source paper "feature based q learning for two player stochastic games"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "sample optimal parametric q learning with linear transition models"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "scalable bilinear pi learning using state and action features"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"

Connected through hyperedge HE-5 (induced by source paper "online planning for decentralized stochastic control with partial history sharing"):
  with members:
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "optimal control strategies in delayed sharing information structures"
    - "policy iteration for decentralized control of markov decision processes"

Connected through hyperedge HE-6 (induced by source paper "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"):
  with members:
    - "communication efficient distributed reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "collaborative learning of stochastic bandits over a social network"
    - "learning to communicate with deep multi agent reinforcement learning"

Question: Please tell me which class the center node 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)