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 "control with distributed deep reinforcement learning learn a better policy"):
  Abstract: Distributed approach is a very effective method to improve training efficiency of reinforcement learning. In this paper, we propose a new heuristic distributed architecture for deep reinforcement learning (DRL) algorithm, in which a PSO based network update mechanism is adopted to speed up learning an optimal policy besides using multiple agents for parallel training. In this mechanism, the update of neural network of each agent is not only according to the training result of itself, but also affected by the optimal neural network of all agents. In order to verify the effectiveness of the proposed method, the proposed architecture is implemented on the Deep Q-Network algorithm (DQN) and the Deep Deterministic Policy Gradient algorithm (DDPG) to train several typical control problems. The training results show that the proposed method is effective.

Includes members:
  Member "accelerated methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "distributed deep reinforcement learning learn how to play atari games in 21 minutes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic experience replay")
  Member "emergent complexity via multi agent competition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "emergence of locomotion behaviours in rich environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "reinforcement learning through asynchronous advantage actor critic on a gpu":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "population guided parallel policy search for reinforcement learning")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)