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: "fully decentralized multi agent reinforcement learning with networked agents"
  Abstract: We consider the problem of \emph{fully decentralized} multi-agent reinforcement learning (MARL), where the agents are located at the nodes of a time-varying communication network. Specifically, we assume that the reward functions of the agents might correspond to different tasks, and are only known to the corresponding agent. Moreover, each agent makes individual decisions based on both the information observed locally and the messages received from its neighbors over the network. Within this setting, the collective goal of the agents is to maximize the globally averaged return over the network through exchanging information with their neighbors. To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large. Under the decentralized structure, the actor step is performed individually by each agent with no need to infer the policies of others. For the critic step, we propose a consensus update via communication over the network. Our algorithms are fully incremental and can be implemented in an online fashion. Convergence analyses of the algorithms are provided when the value functions are approximated within the class of linear functions. Extensive simulation results with both linear and nonlinear function approximations are presented to validate the proposed algorithms. Our work appears to be the first study of fully decentralized MARL algorithms for networked agents with function approximation, with provable convergence guarantees.

Connected through hyperedge HE-1 (induced by source paper "cluster based social reinforcement learning"):
  with members:
    - "message dropout an efficient training method for multi agent deep reinforcement learning"
    - "emergent coordination through competition"
    - "personalization of health interventions using cluster based reinforcement learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "m 3 rl mind aware multi agent management reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "voting based multi agent reinforcement learning for intelligent iot"):
  with members:
    - "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "placement optimization of uav mounted mobile base stations"

Connected through hyperedge HE-3 (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"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "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-4 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "decentralized stochastic gradient tracking for empirical risk minimization"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "stochastic gradient push for distributed deep learning"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "federated optimization distributed optimization beyond the datacenter"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-5 (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"
    - "multi agent reinforcement learning via double averaging primal dual optimization"

Connected through hyperedge HE-6 (induced by source paper "natural actor critic converges globally for hierarchical linear quadratic regulator"):
  with members:
    - "deep reinforcement learning for event driven multi agent decision processes"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "mean field multi agent reinforcement learning"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"

Connected through hyperedge HE-7 (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"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "applications of deep learning and reinforcement learning to biological data"
    - "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-8 (induced by source paper "a deep recurrent q network towards self adapting distributed microservices architecture"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q 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)