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: "communication efficient distributed reinforcement learning"
  Abstract: This paper deals with distributed reinforcement learning (DRL), which involves a central controller and a group of learners. In particular, two DRL settings encountered in several applications are considered: multi-agent reinforcement learning (RL) and parallel RL, where frequent information exchanges between the learners and the controller are required. For many practical distributed systems, however, such as those involving parallel machines for training deep RL algorithms, and multi-robot systems for learning the optimal coordination strategies, the overhead caused by these frequent communication exchanges is considerable, and becomes the bottleneck of the overall performance. To address this challenge, a novel policy gradient method is developed here to cope with such communication-constrained DRL settings. The proposed approach reduces the communication overhead without degrading learning performance by adaptively skipping the policy gradient communication during iterations. It is established analytically that i) the novel algorithm has convergence rate identical to that of the plain-vanilla policy gradient for DRL; while ii) if the distributed computing units are heterogeneous in terms of their reward functions and initial state distributions, the number of communication rounds needed to achieve a desirable learning accuracy is markedly reduced. Numerical experiments on a popular multi-agent RL benchmark corroborate the significant communication reduction attained by the novel algorithm compared to alternatives.

Connected through hyperedge HE-1 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "asynchronous decentralized optimization in directed networks"
    - "distributed metropolis sampler with optimal parallelism"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "distributed off policy actor critic reinforcement learning with policy consensus"
    - "asynchronous subgradient push"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-2 (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"
    - "macro action selection with deep reinforcement learning in starcraft"

Connected through hyperedge HE-3 (induced by source paper "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"):
  with members:
    - "finite sample analyses for fully decentralized multi agent 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"

Connected through hyperedge HE-4 (induced by source paper "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"):
  with members:
    - "a multi agent off policy actor critic algorithm for distributed reinforcement learning"
    - "a theoretical analysis of deep q learning"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "information theoretic considerations in batch reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "dynamic power distribution system management with a locally connected communication network"
    - "distributed equilibrium learning for power network voltage control with a locally connected communication network"
    - "distributed learning of average belief over networks using sequential observations"

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)