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 "flexible functional split and power control for energy harvesting cloud radio access networks"):
  Abstract: Functional split is a promising technique to flexibly balance the processing cost at remote ends and the fronthaul rate in cloud radio access networks (C-RAN). By harvesting renewable energy, remote radio units (RRUs) can save grid power and be flexibly deployed. However, the randomness of energy arrival poses a major design challenge. To maximize the throughput under the average fronthaul rate constraint in C-RAN with renewable powered RRUs, we first study the offline problem of selecting the optimal functional split modes and the corresponding durations, jointly with the transmission power. We find that between successive energy arrivals, at most two functional split modes should be selected. Then the optimal online problem is formulated as an Markov decision process (MDP). To deal with the curse of dimensionality of solving MDP, we further analyze the special case with one instance of energy arrival and two candidate functional split modes as inspired by the offline solution, and then a heuristic online policy is proposed. Numerical results show that with flexible functional split, the throughput can be significantly improved compared with fixed functional split. Also, the proposed heuristic online policy has similar performance with the optimal online one, as validated by simulations.

Includes members:
  Member "optimal placement of baseband functions for energy harvesting virtual small cells":
    also connected through other hyperedges:
      (none)
  Member "on the fronthaul statistical multiplexing gain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "r fft function split at ifft fft in unified lte cran and cable access network")
  Member "online ski rental for on off scheduling of energy harvesting base stations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")
  Member "greendelivery proactive content caching and push with energy harvesting based small cells":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the effective throughput of coded caching a game theoretic perspective")
  Member "redesigning fronthaul for next generation networks beyond baseband samples and point to point links":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the fronthaul statistical multiplexing gain")
  Member "5g wireless backhaul networks challenges and research advances":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "energy harvesting broadband communication systems with processing energy cost":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coded status updates in an energy harvesting erasure channel")
  Member "optimal power allocation for energy harvesting and power grid coexisting wireless communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on mobile edge computing the communication perspective")

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)