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 "multi agent reinforcement learning for energy harvesting two hop communications with full cooperation"):
  Abstract: We focus on energy harvesting (EH) two-hop communications since they are the essential building blocks of more complicated multi-hop networks. The scenario consists of three nodes, where an EH transmitter wants to send data to a receiver through an EH relay. The harvested energy is used exclusively for data transmission and we address the problem of how to efficiently use it. As in practical scenarios, we assume only causal knowledge at the EH nodes, i.e., in each time interval, the transmitter and the relay know their own current and past amounts of incoming energy, battery levels, data buffer levels and channel coefficients for their own transmit channels. Our goal is to find transmission policies which aim at maximizing the throughput considering that the EH nodes fully cooperate with each other to exchange their causal knowledge during a signaling phase. We model the problem as a Markov game and propose a multi-agent reinforcement learning algorithm to find the transmission policies. Furthermore, we show the trade-off between the achievable throughput and the signaling required, and provide convergence guarantees for the proposed algorithm. Results show that even when the signaling overhead is taken into account, the proposed algorithm outperforms other approaches that do not consider cooperation among the nodes.

Includes members:
  Member "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach")
  Member "energy harvesting two hop communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coded status updates in an energy harvesting erasure channel")
  Member "a learning theoretic approach to energy harvesting communication system optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "optimal scheduling and power allocation for two hop energy harvesting communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "single or multiple frames content delivery for robust and secure communication in energy and spectrum efficient 5g networks")
  Member "optimal save then transmit protocol for energy harvesting wireless transmitters":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mimo underlay cognitive radio optimized power allocation effective number of transmit antennas and harvest transmit tradeoff")
  Member "two hop communication with energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "timely updates in energy harvesting two hop networks offline and online policies")
  Member "transmission with energy harvesting nodes in fading wireless channels optimal policies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "closed form analysis of non linear age of information in status updates with an energy harvesting transmitter")
  Member "optimum transmission policies for battery limited energy harvesting nodes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the energy and data storage management in energy harvesting wireless communications")

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.IT(Information Theory)