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: "on the tradeoff between energy harvesting and caching in wireless networks"
  Abstract: Self-powered, energy harvesting small cell base stations (SBS) are expected to be an integral part of next-generation wireless networks. However, due to uncertainties in harvested energy, it is necessary to adopt energy efficient power control schemes to reduce an SBSs' energy consumption and thus ensure quality-of-service (QoS) for users. Such energy-efficient design can also be done via the use of content caching which reduces the usage of the capacity-limited SBS backhaul. of popular content at SBS can also prove beneficial in this regard by reducing the backhaul usage. In this paper, an online energy efficient power control scheme is developed for an energy harvesting SBS equipped with a wireless backhaul and local storage. In our model, energy arrivals are assumed to be Poisson distributed and the popularity distribution of requested content is modeled using Zipf's law. The power control problem is formulated as a (discounted) infinite horizon dynamic programming problem and solved numerically using the value iteration algorithm. Using simulations, we provide valuable insights on the impact of energy harvesting and caching on the energy and sum-throughput performance of the SBS as the network size is varied. Our results also show that the size of cache and energy harvesting equipment at the SBS can be traded off, while still meeting the desired system performance.

Connected through hyperedge HE-1 (induced by source paper "the 5g cellular backhaul management dilemma to cache or to serve"):
  with members:
    - "inter operator resource management for millimeter wave multi hop backhaul networks"
    - "optimal dynamic multicast scheduling for cache enabled content centric wireless networks"
    - "optimal geographic caching in cellular networks"
    - "caching at the edge a green perspective for 5g networks"
    - "matching theory for backhaul management in small cell networks with mmwave capabilities"
    - "multicast aware caching for small cell networks"
    - "what will 5g be"
    - "living on the edge the role of proactive caching in 5g wireless networks"

Connected through hyperedge HE-2 (induced by source paper "policy optimization for content push via energy harvesting small cells in heterogeneous networks"):
  with members:
    - "stochastic content centric multicast scheduling for cache enabled heterogeneous cellular networks"
    - "wireless caching technical misconceptions and business barriers"
    - "greendelivery proactive content caching and push with energy harvesting based small cells"
    - "proactive push with energy harvesting based small cells in heterogeneous networks"
    - "energy harvesting small cell networks feasibility deployment and operation"
    - "learning based optimization of cache content in a small cell base station"
    - "multicast aware caching for small cell networks"
    - "cache enabled small cell networks modeling and tradeoffs"

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)