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: "performance impact of idle mode capability on dense small cell networks with los and nlos transmissions"
  Abstract: In dense small cell networks (SCNs), a large number of base stations (BSs) can be put to idle modes without signal transmission, if there is no active user equipment (UE) within their coverage areas. Setting those BSs to idle modes can mitigate unnecessary inter-cell interference and reduce energy consumption. Such idle mode feature at BSs is referred to as the idle mode capability (IMC) and it can largely improve the performance of the 5th-generation (5G) networks. In this paper, we study the performance impact of the BS IMC on dense SCNs. Different from existing work, we consider a sophisticated and more realistic path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Analytical results are obtained for the coverage probability, the area spectral efficiency and the energy efficiency performance for SCNs with the BS IMC. An upper bound, a lower bound and an approximate expression of the density of the non-idle BSs are also derived. The performance impact of the IMC on network densification is shown to be significant. As the BS density surpasses the UE density, thus creating a surplus of BSs, the coverage probability will continuously increase toward one, which addresses the critical issue of coverage probability decrease caused by the LoS/NLoS transmissions. The results derived from our analysis shed valuable new light on the deployment and the operation of future dense SCNs in 5G.

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"
    - "microscopic analysis of the uplink interference in fdma small cell networks"
    - "performance impact of idle mode capability on dense small cell networks"
    - "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 "what is the optimal network deployment for a fixed density of antennas"):
  with members:
    - "on the performance of practical ultra dense networks the major and minor factors"
    - "heterogeneous cellular networks with los and nlos transmissions the role of massive mimo and small cells"
    - "study on the idle mode capability with los and nlos transmissions"
    - "what is the true value of dynamic tdd a mac layer perspective"
    - "please lower small cell antenna heights in 5g"
    - "performance impact of idle mode capability on dense small cell networks"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "massive mimo ten myths and one critical question"

Connected through hyperedge HE-3 (induced by source paper "what is the true value of dynamic tdd a mac layer perspective"):
  with members:
    - "ultra dense networks is there a limit to spatial spectrum reuse"
    - "study on the idle mode capability with los and nlos transmissions"
    - "throughput and coverage for a mixed full and half duplex small cell network"
    - "performance impact of idle mode capability on dense small cell networks"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "d2d enhanced heterogeneous cellular networks with dynamic tdd"
    - "5g ultra dense 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.IT(Information Theory)