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 "resource management in wireless networks via multi agent deep reinforcement learning"):
  Abstract: We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning (RL) for interference mitigation in wireless networks. We equip each transmitter in the network with a deep RL agent, which receives partial delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval. Our proposed framework enables the agents to make decisions simultaneously and in a distributed manner, without any knowledge about the concurrent decisions of other agents. Moreover, our design of the agents' observation and action spaces is scalable, in the sense that an agent trained on a scenario with a specific number of transmitters and receivers can be readily applied to scenarios with different numbers of transmitters and/or receivers. Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and $5^{th}$ percentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized information-theoretic scheduling algorithm. We also show that our trained agents are robust and maintain their performance gains when experiencing mismatches between training and testing deployments.

Includes members:
  Member "spectrum sharing in vehicular networks based on multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "dota 2 with large scale deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "on the power of curriculum learning in training deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "curriculum deepsdf")
  Member "a deep q learning method for downlink power allocation in multi cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges")
  Member "deep reinforcement learning for distributed uncoordinated cognitive radios resource allocation":
    also connected through other hyperedges:
      (none)
  Member "zpd teaching strategies for deep reinforcement learning from demonstrations":
    also connected through other hyperedges:
      (none)
  Member "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "quantifying generalization in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)