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: "renewable powered cellular networks energy field modeling and network coverage"
  Abstract: Powering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This warrants research on quantifying the aforementioned negative effect and designing countermeasure techniques, motivating the current work. First, a novel energy field model is presented, in which fixed maximum energy intensity    $\gamma$   occurs at Poisson distributed locations, called  energy centers . The intensities fall off from the centers following an exponential decay function of squared distance and the energy intensity at an arbitrary location is given by the decayed intensity from the nearest energy center. The product between the energy center density and the exponential rate of the decay function, denoted as    $\psi$  , is shown to determine the energy field distribution. Next, the paper considers a cellular downlink network powered by harvesting energy from the energy field and analyzes its network coverage. For the case of harvesters deployed at the same sites as base stations (BSs), as    $\gamma$   increases, the mobile outage probability is shown to scale as    $(c\gamma^{-\pi\psi}+p)$  , where    $p$   is the outage probability corresponding to a flat energy field and    $c$   is a constant. Subsequently, a simple scheme is proposed for counteracting the energy randomness by spatial averaging. Specifically, distributed harvesters are deployed in clusters and the generated energy from the same cluster is aggregated and then redistributed to BSs. As the cluster size increases, the power supplied to each BS is shown to converge to a constant proportional to the number of harvesters per BS. Several additional issues are investigated in this paper, including regulation of the power transmission loss in energy aggregation and extensions of the energy field model.

Connected through hyperedge HE-1 (induced by source paper "self sustainability of energy harvesting systems concept analysis and design"):
  with members:
    - "performance analysis of ambient rf energy harvesting with repulsive point process modeling"
    - "energy harvesting wireless communications a review of recent advances"
    - "rf powered cognitive radio networks technical challenges and limitations"
    - "wireless networks with rf energy harvesting a contemporary survey"
    - "on green energy powered cognitive radio networks"
    - "a cross layer perspective on energy harvesting aided green communications over fading channels"

Connected through hyperedge HE-2 (induced by source paper "energy harvesting wireless communications a review of recent advances"):
  with members:
    - "dynamic compression transmission for energy harvesting multihop networks with correlated sources"
    - "constrained codes for joint energy and information transfer"
    - "energy harvesting broadband communication systems with processing energy cost"
    - "spatial throughput of mobile ad hoc networks powered by energy harvesting"
    - "energy cooperation in energy harvesting communications"
    - "interactive joint transfer of energy and information"
    - "opportunistic wireless energy harvesting in cognitive radio networks"
    - "throughput maximization for the gaussian relay channel with energy harvesting constraints"

Connected through hyperedge HE-3 (induced by source paper "device to device communication underlaying a finite cellular network region"):
  with members:
    - "performance analysis of arbitrarily shaped underlay cognitive networks effects of secondary user activity protocols"
    - "an analytical framework for device to device communication in cellular networks"
    - "operational region of d2d communications for enhancing cellular network performance"
    - "on fundamental trade offs of device to device communications in large wireless networks"
    - "a geometrical based throughput bound analysis for device to device communications in cellular networks"
    - "a survey on device to device communication in cellular networks"
    - "downlink cellular network analysis with multi slope path loss models"
    - "spectrum sharing for device to device communication in cellular networks"

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)