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: "multi agent reinforcement learning via double averaging primal dual optimization"
  Abstract: Despite the success of single-agent reinforcement learning, multi-agent reinforcement learning (MARL) remains challenging due to complex interactions between agents. Motivated by decentralized applications such as sensor networks, swarm robotics, and power grids, we study policy evaluation in MARL, where agents with jointly observed state-action pairs and private local rewards collaborate to learn the value of a given policy. In this paper, we propose a double averaging scheme, where each agent iteratively performs averaging over both space and time to incorporate neighboring gradient information and local reward information, respectively. We prove that the proposed algorithm converges to the optimal solution at a global geometric rate. In particular, such an algorithm is built upon a primal-dual reformulation of the mean squared projected Bellman error minimization problem, which gives rise to a decentralized convex-concave saddle-point problem. To the best of our knowledge, the proposed double averaging primal-dual optimization algorithm is the first to achieve fast finite-time convergence on decentralized convex-concave saddle-point problems.

Connected through hyperedge HE-1 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "on applications of bootstrap in continuous space reinforcement learning"
    - "on optimality of adaptive linear quadratic regulators"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "learning unknown markov decision processes a thompson sampling approach"

Connected through hyperedge HE-2 (induced by source paper "behavior planning for connected autonomous vehicles using feedback deep reinforcement learning"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "control of connected and automated vehicles state of the art and future challenges"
    - "virtual to real reinforcement learning for autonomous driving"
    - "end to end driving via conditional imitation learning"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "convergent policy optimization for safe reinforcement learning"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "provably efficient q learning with low switching cost"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "stochastic successive convex approximation for non convex constrained stochastic optimization"
    - "learning safe policies with expert guidance"
    - "communication efficient distributed reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "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"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-5 (induced by source paper "voting based multi agent reinforcement learning"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "applications of deep learning and reinforcement learning to biological data"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "placement optimization of uav mounted mobile base stations"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"

Connected through hyperedge HE-6 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "on convergence of emphatic temporal difference learning"
    - "distributed policy evaluation under multiple behavior strategies"

Connected through hyperedge HE-7 (induced by source paper "finite sample analysis of decentralized temporal difference learning with linear function approximation"):
  with members:
    - "reanalysis of variance reduced temporal difference learning"
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "two timescale voltage control in distribution grids using deep reinforcement learning"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "finite time performance bounds and adaptive learning rate selection for two time scale reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-8 (induced by source paper "distributed off policy actor critic reinforcement learning with policy consensus"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "counterfactual multi agent policy gradients"
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "distributed policy evaluation under multiple behavior strategies"
    - "playing atari with deep reinforcement learning"
    - "off policy actor critic"

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)