human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks"):
  Abstract: This paper proposes a \emph{fully asynchronous} scheme for policy evaluation of distributed reinforcement learning (DisRL) over peer-to-peer networks. Without any form of coordination, nodes can communicate with neighbors and compute their local variables using (possibly) delayed information at any time, which is in sharp contrast to the asynchronous gossip. Thus, the proposed scheme fully takes advantage of the distributed setting. We prove that our method converges at a linear rate $\mathcal{O}(c^k)$ where $c\in(0,1)$ and $k$ increases by one no matter on which node updates, showing the computational advantage by reducing the amount of synchronization. Numerical experiments show that our method speeds up linearly w.r.t. the number of nodes, and is robust to straggler nodes. To the best of our knowledge, our work is the first theoretical analysis for asynchronous update in DisRL, including the \emph{parallel RL} domain advocated by A3C.

Includes members:
  Member "multi agent reinforcement learning a selective overview of theories and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "asynchronous decentralized optimization in directed networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "moniqua modulo quantized communication in decentralized sgd")
  Member "distributed metropolis sampler with optimal parallelism":
    also connected through other hyperedges:
      (none)
  Member "value propagation for decentralized networked deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimization for reinforcement learning from single agent to cooperative agents")
  Member "distributed off policy actor critic reinforcement learning with policy consensus":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi agent off policy actor critic algorithm for distributed reinforcement learning")
  Member "asynchronous subgradient push":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "central server free federated learning over single sided trust social networks")
  Member "impala scalable distributed deep rl with importance weighted actor learner architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "communication efficient distributed reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convergent policy optimization for safe reinforcement learning")

Question: Please tell me which class the source paper 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)