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 "unified stochastic geometry modeling and analysis of cellular networks in los nlos and shadowed fading"):
  Abstract: Statistical characterization of the signal-to-interference-plus-noise ratio (SINR) via its cumulative distribution function (CDF) is ubiquitous in a vast majority of technical contributions in the area of cellular networks since it boils down to averaging the Laplace transform of the aggregate interference, a benefit accorded at the expense of confinement to the simplistic Rayleigh fading. In this work, to capture diverse fading channels that appear in realistic outdoor/indoor wireless communication scenarios, we tackle the problem differently. By exploting the moment generating function (MGF) of the SINR, we succeed in analytically assessing cellular networks performance over the shadowed {\kappa}-{\mu}, {\kappa}-{\mu} and {\eta}-{\mu} fading models. The latter offer high flexibility by capturing diverse fading channels including Rayleigh, Nakagami-m, Rician, and Rician shadow fading distributions. These channel models have been recently praised for their capability to accurately model dense urban environments, future femtocells and device-to-device (D2D) shadowed channels. In addition to unifying the analysis for different channel models, this work integrates, for the first time, the coverage, the achievable rate, and the bit error probability (BEP), which are largely treated separately in the literature. The developed model and analysis are validated over a broad range of simulation setups and parameters.

Includes members:
  Member "modeling and analysis of cellular networks using stochastic geometry a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "unified stochastic geometry model for mimo cellular networks with retransmissions":
    also connected through other hyperedges:
      (none)
  Member "coverage analysis in downlink poisson cellular network with kappa mu shadowed fading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transmit power policy and ergodic multicast rate analysis of cognitive radio networks in generalized fading")
  Member "modeling cellular networks in fading environments with dominant specular components":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a unified asymptotic analysis of area spectral efficiency in ultradense cellular networks")
  Member "on modeling coverage and rate of random cellular networks under generic channel fading":
    also connected through other hyperedges:
      (none)
  Member "optimizing user association and spectrum allocation in hetnets a utility perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resource allocation in green dense cellular networks complexity and algorithms")
  Member "hethetnets heterogeneous traffic distribution in heterogeneous wireless cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deployment of drone base stations for cellular communication without apriori user distribution information")
  Member "downlink cellular network analysis with multi slope path loss models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)