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: "distributed reinforcement learning via gossip"
  Abstract: We consider the classical TD(0) algorithm implemented on a network of agents wherein the agents also incorporate the updates received from neighboring agents using a gossip-like mechanism. The combined scheme is shown to converge for both discounted and average cost problems.

Connected through hyperedge HE-1 (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"
    - "fully decentralized multi agent reinforcement learning with networked agents"

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"
    - "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-3 (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"
    - "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"

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"
    - "communication efficient distributed reinforcement learning"
    - "distributed equilibrium learning for power network voltage control with a locally connected communication network"

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.DC(Distributed, Parallel, and Cluster Computing)