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 "uplink performance analysis of dense cellular networks with los and nlos transmissions"):
  Abstract: In this paper, we analyse the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in our study: (i) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; (ii) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent Homogeneous Poisson point processes (HPPPs); and (iii) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 10^1-10^3 BSs/km^2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 10^2-10^3 BSs/km^2, while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 10^1-10^2 BSs/km^2.

Includes members:
  Member "impact of los nlos propagation and path loss in ultra dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "delay analysis of spatially coded mimo zfbf with retransmissions in random networks")
  Member "performance impact of los and nlos transmissions in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "5g ultra dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a spatiotemporal framework for information freshness in iot uplink networks")
  Member "analytical modeling of uplink cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic tdd interference analysis in macro cell and small cell deployments")
  Member "capacity analysis of a multi cell multi antenna cooperative cellular network with co channel interference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "smart contract based secure spectrum sharing in multi operators wireless communication networks")
  Member "a tractable approach to coverage and rate in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)