human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "on the energy and data storage management in energy harvesting wireless communications"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "wireless network intelligence at the edge"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"

Connected through hyperedge HE-2 (induced by source paper "resource allocation in green dense cellular networks complexity and algorithms"):
  with members:
    - "large scale spectrum allocation for cellular networks via sparse optimization"
    - "optimal user scheduling in energy harvesting wireless networks"
    - "scalable spectrum allocation and user association in networks with many small cells"
    - "boost base station on off switching strategy for energy efficient massive mimo hetnets"
    - "online energy efficient packet scheduling for a common deadline with and without energy harvesting"
    - "gate greening at the edges"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"

Connected through hyperedge HE-3 (induced by source paper "flexible functional split and power control for energy harvesting cloud radio access networks"):
  with members:
    - "optimal placement of baseband functions for energy harvesting virtual small cells"
    - "on the fronthaul statistical multiplexing gain"
    - "greendelivery proactive content caching and push with energy harvesting based small cells"
    - "redesigning fronthaul for next generation networks beyond baseband samples and point to point links"
    - "5g wireless backhaul networks challenges and research advances"
    - "energy harvesting broadband communication systems with processing energy cost"
    - "optimal power allocation for energy harvesting and power grid coexisting wireless communication systems"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-4 (induced by source paper "delay aware wireless network coding in adversarial traffic"):
  with members:
    - "online energy efficient scheduling for timely information downloads in mobile networks"
    - "network utility maximization in adversarial environments"
    - "adversarial network coding"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"
    - "optimal reverse carpooling over wireless networks a distributed optimization approach"
    - "network coding aware queue management for tcp flows over coded wireless networks"

Connected through hyperedge HE-5 (induced by source paper "energy sustainable paradigms and methods for future mobile networks a survey"):
  with members:
    - "information exchange in randomly deployed dense wsns with wireless energy harvesting capabilities"
    - "a stochastic geometry based demand response management framework for cellular networks powered by smart grid"
    - "fog computing based radio access networks issues and challenges"
    - "a hybrid energy sharing framework for green cellular networks"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "green networking in cellular hetnets a unified radio resource management framework with base station on off switching"
    - "dynamic clustering and on off strategies for wireless small cell networks"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"

Connected through hyperedge HE-6 (induced by source paper "optimal placement of baseband functions for energy harvesting virtual small cells"):
  with members:
    - "nfv state of the art challenges and implementation in next generation mobile networks vepc"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "solarstat modeling photovoltaic sources through stochastic markov processes"

Connected through hyperedge HE-7 (induced by source paper "power availability aware cell association for energy harvesting small cell base stations"):
  with members:
    - "distributed user association in energy harvesting small cell networks an exchange economy with uncertainty"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "distributed multi relay selection in accumulate then forward energy harvesting relay networks"
    - "user association in 5g networks a survey and an outlook"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "ambient rf energy harvesting in ultra dense small cell networks performance and trade offs"
    - "millimeter wave energy harvesting"
    - "relay selection in wireless powered cooperative networks with energy storage"

Question: Please tell me which class the center node 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)