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 "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.

Includes members:
  Member "dynamic power distribution system management with a locally connected communication network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents")
  Member "learning and management for internet of things accounting for adaptivity and scalability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal design of queuing systems via compositional stochastic programming")
  Member "stochastic variance reduced policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "fully decentralized multi agent reinforcement learning with networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cluster based social reinforcement learning")
  Member "deep decentralized multi task multi agent reinforcement learning under partial observability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "a berkeley view of systems challenges for ai":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "large scale discrete fourier transform on tpus")
  Member "multi agent actor critic for mixed cooperative competitive environments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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)