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: "microscopic analysis of the uplink interference in fdma small cell networks"
  Abstract: In this paper, we analytically derive an upper bound on the error in approximating the uplink (UL) single-cell interference by a lognormal distribution in frequency division multiple access (FDMA) small cell networks (SCNs). Such an upper bound is measured by the Kolmogorov-Smirnov (KS) distance between the actual cumulative density function (CDF) and the approximate CDF. The lognormal approximation is important because it allows tractable network performance analysis. Our results are more general than the existing works in the sense that we do not pose any requirement on 1) the shape and/or size of cell coverage areas; 2) the uniformity of user equipment (UE) distribution; and 3) the type of multipath fading. Based on our results, we propose a new framework to directly and analytically investigate a complex network with practical deployment of multiple BSs placed at irregular locations, using a power lognormal approximation of the aggregate UL interference. The proposed interference analysis is particularly useful for the 5th generation (5G) systems with more general cell deployment and UE distribution beyond the widely used Poisson distribution.

Connected through hyperedge HE-1 (induced by source paper "promises and caveats of uplink iot ultra dense networks"):
  with members:
    - "performance impact of base station antenna heights in dense cellular networks"
    - "on the fundamental characteristics of ultra dense small cell networks"
    - "performance impact of idle mode capability on dense small cell networks"
    - "performance impact of idle mode capability on dense small cell networks with los and nlos transmissions"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "uplink performance analysis of dense cellular networks with los and nlos transmissions"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "downlink cellular network analysis with multi slope path loss models"

Connected through hyperedge HE-2 (induced by source paper "the major and minor factors in the performance analysis of ultra dense networks"):
  with members:
    - "study on the idle mode capability with los and nlos transmissions"
    - "nearest neighbour distance distribution in hard core point processes"
    - "throughput and coverage for a mixed full and half duplex small cell network"
    - "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks"
    - "what is the true value of dynamic tdd a mac layer perspective"
    - "please lower small cell antenna heights in 5g"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "uplink performance analysis of dense cellular networks with los and nlos transmissions"

Connected through hyperedge HE-3 (induced by source paper "on the performance of practical ultra dense networks the major and minor factors"):
  with members:
    - "study on the idle mode capability with los and nlos transmissions"
    - "throughput and coverage for a mixed full and half duplex small cell network"
    - "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "stochastic geometry modeling and analysis of multi tier millimeter wave cellular networks"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "d2d enhanced heterogeneous cellular networks with dynamic tdd"
    - "study on scheduling techniques for ultra dense small cell networks"

Connected through hyperedge HE-4 (induced by source paper "performance impact of idle mode capability on dense small cell networks"):
  with members:
    - "dynamic base station repositioning to improve performance of drone small cells"
    - "study on the idle mode capability with los and nlos transmissions"
    - "handover management in dense cellular networks a stochastic geometry approach"
    - "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks"
    - "please lower small cell antenna heights in 5g"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"

Connected through hyperedge HE-5 (induced by source paper "dna ga a new approach of network performance analysis"):
  with members:
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "approximation of uplink inter cell interference in fdma small cell networks"
    - "analytical modeling of uplink 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)