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: "traffic hotspot localization in 3g and 4g wireless networks using omc metrics"
  Abstract: In recent years, there has been an increasing awareness to traffic localization techniques driven by the emergence of heterogeneous networks (HetNet) with small cells deployment and the green networks. The localization of hotspot data traffic with a very high accuracy is indeed of great interest to know where the small cells should be deployed and how can be managed for sleep mode concept. In this paper, we propose a new traffic localization technique based on the combination of different key performance indicators (KPI) extracted from the operation and maintenance center (OMC). The proposed localization algorithm is composed with five main steps; each one corresponds to the determination of traffic weight per area using only one KPI. These KPIs are Timing Advance (TA), Angle of Arrival (AoA), Neighbor cell level, the load of each cell and the Harmonic mean throughput (HMT) versus the Arithmetic mean throughput (AMT). The five KPIs are finally combined by a function taking as variables the values computed from the five steps. By mixing such KPIs, we show that it is possible to lessen significantly the errors of localization in a high precision attaining small cell dimensions.

Connected through hyperedge HE-1 (induced by source paper "system level analysis of heterogeneous networks under imperfect traffic hotspot localization"):
  with members:
    - "analytical tractability of hexagonal network model with random user location"
    - "5g wireless backhaul networks challenges and research advances"
    - "spectrum and energy efficiency evaluation of two tier femtocell networks with partially open channels"
    - "emerging technologies and research challenges for 5g wireless networks"
    - "modeling heterogeneous network interference using poisson point processes"
    - "modeling and analysis of k tier downlink heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "offloading traffic hotspots using moving small cells"):
  with members:
    - "system level analysis of heterogeneous networks under imperfect traffic hotspot localization"
    - "analytical tractability of hexagonal network model with random user location"
    - "performance analysis of small cells deployment under imperfect traffic hotspot localization"
    - "emerging technologies and research challenges for 5g wireless networks"
    - "modeling heterogeneous network interference using poisson point processes"
    - "modeling and analysis of k tier downlink heterogeneous cellular networks"

Connected through hyperedge HE-3 (induced by source paper "analytical tractability of hexagonal network model with random user location"):
  with members:
    - "system level analysis of heterogeneous networks under imperfect traffic hotspot localization"
    - "asymptotic deployment gain a simple approach to characterize the sinr distribution in general cellular networks"
    - "the mean interference to signal ratio and its key role in cellular and amorphous networks"
    - "the ginibre point process as a model for wireless networks with repulsion"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-4 (induced by source paper "performance analysis of small cells deployment under imperfect traffic hotspot localization"):
  with members:
    - "modeling heterogeneous network interference using poisson point processes"
    - "modeling and analysis of k tier downlink heterogeneous 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)