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: "opportunistic sleep mode strategies in wireless small cell networks"
  Abstract: The design of energy-efficient mechanisms is one of the key challenges in emerging wireless small cell networks. In this paper, a novel approach for opportunistically switching ON/OFF base stations to improve the energy efficiency in wireless small cell networks is proposed. The proposed approach enables the small cell base stations to optimize their downlink performance while balancing the load among each another, while satisfying their users’ quality-of-service requirements. The problem is formulated as a noncooperative game among the base stations that seek to minimize a cost function which captures the tradeoff between energy expenditure and load. To solve this game, a distributed learning algorithm is proposed using which the base stations autonomously choose their optimal transmission strategies. Simulation results show that the proposed approach yields significant performance gains in terms of reduced energy expenditures up to 23% and reduced load up to 40% compared to conventional approaches.

Connected through hyperedge HE-1 (induced by source paper "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"):
  with members:
    - "a multi game framework for harmonized lte u and wifi coexistence over unlicensed bands"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"
    - "holistic small cell traffic balancing across licensed and unlicensed bands"
    - "modeling and analyzing the coexistence of wi fi and lte in unlicensed spectrum"
    - "dynamic clustering and on off strategies for wireless small cell networks"
    - "why to decouple the uplink and downlink in cellular networks and how to do it"
    - "small cell traffic balancing over licensed and unlicensed bands"
    - "joint rate and sinr coverage analysis for decoupled uplink downlink biased cell associations in hetnets"

Connected through hyperedge HE-2 (induced by source paper "uav assisted heterogeneous networks for capacity enhancement"):
  with members:
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "drone small cells in the clouds design deployment and performance analysis"
    - "performance analysis of micro unmanned airborne communication relays for cellular networks"
    - "throughput and energy efficiency analysis of small cell networks with multi antenna base stations"

Connected through hyperedge HE-3 (induced by source paper "capacity analysis of lte advanced hetnets with reduced power subframes and range expansion"):
  with members:
    - "joint resource partitioning and offloading in heterogeneous cellular networks"
    - "offloading in heterogeneous networks modeling analysis and design insights"
    - "modeling heterogeneous network interference using poisson point processes"
    - "modeling and analysis of k tier downlink heterogeneous cellular networks"
    - "enhanced intercell interference coordination challenges in heterogeneous networks"
    - "mean interference in hard core wireless networks"
    - "downlink capacity and base station density in cellular networks"
    - "a tractable approach to coverage and rate 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.NI(Networking and Internet Architecture)