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 "analytical and simulation performance of a typical user in random cellular network"):
  Abstract: Spatial Poisson Point Process (PPP) network, whose Base Stations (BS)s are distributed according to a Poisson distribution, is currently used as a accurate model to analyse the performance of a cellular network. Most current work on evaluation of PPP network in Rayleigh fading channels are usually assumed that the BSs have fixed transmission power levels and there is only a Resource Block (RB) or a user in each cell. In this paper, the Rayleigh-Lognormal fading channels are considered, and it is assumed that each cell is allocated $N$ Resource Blocks (RB) to serve M users. Furthermore, the serving and interfering BS of a typical user are assumed to transmit at different power levels. The closed-form expression for the network coverage probability for both low and high SNR is derived by using Gauss-Legendre approximation. The analytical results indicates that the performance of the typical user is proportional to the transmission power and density of BSs when SNR 10 dB or {\lambda}>1. The variance of Monte Carlo simulation is considered to verify the stability and accuracy of simulation results.

Includes members:
  Member "average rate of downlink heterogeneous cellular networks over generalized fading channels a stochastic geometry approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatio temporal correlation of interference in manet under spatially correlated shadowing environment")
  Member "modeling and analysis of k tier downlink heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense 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)