human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "performance impact of los and nlos transmissions in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mobility in the sky performance and mobility analysis for cellular connected uavs")
  Member "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "downlink cellular network analysis with multi slope path loss models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "coverage and rate analysis for millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "offloading in heterogeneous networks modeling analysis and design insights":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come")
  Member "coverage and economy of cellular networks with many base stations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coverage analysis of 3 d dense cellular networks with realistic propagation conditions")
  Member "a tractable approach to coverage and rate in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")

Question: Please tell me which class the source paper 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)