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 "finite sample analysis of decentralized temporal difference learning with linear function approximation"):
  Abstract: Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an `additional' projection step to control the `gradient' bias incurred by the Markovian observations. In this paper, we provide a finite-sample analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type---in the sense that they hold under the most practical assumptions ---which is made possible by means of a novel multi-step Lyapunov analysis.

Includes members:
  Member "reanalysis of variance reduced temporal difference learning":
    also connected through other hyperedges:
      (none)
  Member "two time scale off policy td learning non asymptotic analysis over markovian samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "deep reinforcement learning for adaptive caching in hierarchical content delivery networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross layer scheduling and beamforming in smart grid powered cellular networks with heterogeneous energy coordination")
  Member "nonconvex optimization meets low rank matrix factorization an overview":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "two timescale voltage control in distribution grids using deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning connectivity and higher order interactions in radial distribution grids")
  Member "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "finite time performance bounds and adaptive learning rate selection for two time scale reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory")
  Member "multi agent reinforcement learning via double averaging primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")

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)