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 "online ski rental for on off scheduling of energy harvesting base stations"):
  Abstract: The co-existence of small cell base stations (SBSs) with conventional macrocell base station is a promising approach to boost the capacity and coverage of cellular networks. However, densifying the network with a viral deployment of SBSs can significantly increase energy consumption. To reduce the reliance on unsustainable energy sources, one can adopt self-powered SBSs that rely solely on energy harvesting. Due to the uncertainty of energy arrival and the finite capacity of energy storage systems, self-powered SBSs must smartly optimize their ON and OFF schedule. In this paper, the problem of ON/OFF scheduling of self-powered SBSs is studied, in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs consisted of energy consumption and transmission delay of a network. For the original problem, we show an algorithm can solve the problem in the illustrative case. To reduce the complexity of the original problem, an approximation is proposed. To solve the approximated problem, a novel approach based on the ski rental framework, a powerful online optimization tool, is proposed. Using this approach, each SBS can effectively decide on its ON/OFF schedule autonomously, without any prior information on future energy arrivals. By using competitive analysis, a deterministic online algorithm (DOA) and a randomized online algorithm (ROA) are developed. ROA is shown to achieve the optimal competitive ratio in the approximation problem. Simulation results show that, compared to a baseline approach, the ROA can yield performance gains reaching up to 15.6% in terms of reduced total energy consumption of SBSs and up to 20.6% in terms of per-SBS network delay reduction. The results shed light on the fundamental aspects that impact the ON time of SBSs while demonstrating that the proposed ROA can reduce up to 69.9% the total cost compared to a baseline approach.

Includes members:
  Member "distributed user association in energy harvesting small cell networks a probabilistic bandit model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficient optimization of wireless powered 5g full duplex cellular networks a mean field game approach")
  Member "dynamic clustering and on off strategies for wireless small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "gate greening at the edges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource allocation in green dense cellular networks complexity and algorithms")
  Member "millimeter wave cellular networks a mac layer perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi user provisioning in millimeter wave urban cellular networks")
  Member "energy harvesting small cell networks feasibility deployment and operation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficient optimization of wireless powered 5g full duplex cellular networks a mean field game approach")
  Member "5g wireless backhaul networks challenges and research advances":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "dynamic clustering and sleep mode strategies for small cell networks":
    also connected through other hyperedges:
      (none)
  Member "millimeter wave cellular wireless networks potentials and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")

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)