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: "gate greening at the edges"
  Abstract: Dramatic data traffic growth, especially in wireless data, is driving a significant surge in energy consumption in the last mile access of the telecommunications infrastructure. The growing energy consumption not only escalates operators' operational expenditures, but also leads to a significant rise in carbon footprints. Therefore, enhancing the energy efficiency of broadband access networks is becoming a necessity to bolster social, environmental, and economic sustainability. This article provides an overview on the design and optimization of energy-efficient broadband access networks, analyzes the energy-efficient design of passive optical networks, discusses the enabling technologies for next generation broadband wireless access networks, and elicits emerging technologies for enhancing the energy efficiency of the last mile access of the network infrastructure.

Connected through hyperedge HE-1 (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 ski rental for on off scheduling of energy harvesting base stations"
    - "online energy efficient packet scheduling for a common deadline with and without energy harvesting"
    - "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-2 (induced by source paper "online ski rental for on off scheduling of energy harvesting base stations"):
  with members:
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "dynamic clustering and on off strategies for wireless small cell networks"
    - "millimeter wave cellular networks a mac layer perspective"
    - "energy harvesting small cell networks feasibility deployment and operation"
    - "5g wireless backhaul networks challenges and research advances"
    - "dynamic clustering and sleep mode strategies for small cell networks"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "base station sleeping and resource allocation in renewable energy powered cellular networks"

Connected through hyperedge HE-3 (induced by source paper "online ski rental for on off scheduling of energy harvesting millimeter wave base stations"):
  with members:
    - "millimeter wave cellular networks a mac layer perspective"
    - "energy harvesting small cell networks feasibility deployment and operation"
    - "5g wireless backhaul networks challenges and research advances"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "base station sleeping and resource allocation in renewable energy powered cellular networks"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "analysis of blockage effects on urban 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.NI(Networking and Internet Architecture)