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 "distributed power control for delay optimization in energy harvesting cooperative relay networks"):
  Abstract: We consider cooperative communications with energy harvesting (EH) relays and develop a distributed power control mechanism for the relaying terminals. Unlike prior work, which mainly deals with single-relay systems with saturated traffic flow, we address the case of bursty data arrival at the source cooperatively forwarded by multiple half-duplex EH relays. We aim at optimizing the long-run average delay of the source packets under the energy neutrality constraint on the power consumption of each relay. While EH relay systems have been predominantly optimized using either offline or online methodologies, we take on a more realistic learning-theoretic approach. Hence, our scheme can be deployed for real-time operation without assuming acausal information on channel realizations, data/energy arrivals as required by offline optimization, or relying on precise statistics of the system processes, as is the case with online optimization. We formulate the problem as a partially observable identical payoff stochastic game (PO-IPSG) with factored controllers in which the power control policy of each relay is adaptive to its channel and energy states as well as to the state of the source buffer. We equip each relay with a reinforcement learning procedure and prove that the parallel execution of this procedure is convergent to (at least) a locally optimal solution of the formulated PO-IPSG. The proposed algorithm operates without explicit message exchanges between the relays, while inducing only little source-relay signaling overhead. By simulation, we contrast the delay performance of the proposed method against existing heuristics for throughput maximization. It is shown that compared with these heuristics, the systematic approach adopted in this paper has a smaller suboptimality gap once evaluated against a centralized optimal policy armed with perfect statistics.

Includes members:
  Member "energy harvesting two hop communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coded status updates in an energy harvesting erasure channel")
  Member "learning to cooperate via policy search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "power allocation strategies in energy harvesting wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on maximizing information reliability in wireless powered cooperative networks")
  Member "power allocation for conventional and buffer aided link adaptive relaying systems with energy harvesting nodes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient resource allocation for time varying ofdma relay systems with hybrid energy supplies")
  Member "on green energy powered cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "harvest or transmit policy for cognitive radio networks a learning theoretic approach")
  Member "throughput maximization for the gaussian relay channel with energy harvesting constraints":
    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 "optimal scheduling and power allocation for two hop energy harvesting communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "single or multiple frames content delivery for robust and secure communication in energy and spectrum efficient 5g networks")
  Member "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")

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.NI(Networking and Internet Architecture)