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 "the major and minor factors in the performance analysis of ultra dense networks"):
  Abstract: In this paper, we conduct performance evaluation for ultra-dense networks (UDNs) and identify the major and minor factors in the performance analysis of UDNs using stochastic geometry. From our study, we draw the following conclusions. First, there are 3 major factors that define the fundamental behaviors of UDNs: (i) a multi-piece path loss model with line-of-sight (LoS)/non-line-of-sight (NLoS) transmissions, which leads to a performance degradation caused by a faster growth of the interference power compared with the signal power; (ii) a non-zero antenna height difference between BSs and UEs, which gives rise to another performance degradation due to a cap on the signal power; (iii) a finite BS/UE density, which promises a performance improvement due to the BS idle mode operation that mitigates unnecessary inter-cell interference. Second, there are 4 minor factors that do not change the fundamental behaviors of UDNs: (i) a general multi-path fading model based on Rician fading; (ii) a correlated shadow fading model; (iii) a base station (BS) density dependent transmission power; (iv) a deterministic BS/user density. Finally, there are 3 factors for future study: (i) a BS vertical antenna pattern; (ii) multi-antenna and/or multi-BS joint transmissions; (iii) a constrained uniform distribution of BSs. Our conclusions can guide researchers to down-select the assumptions in their stochastic geometry analyses, so as to avoid unnecessarily complicated results, while still capturing the fundamentals of UDNs in a meaningful way.

Includes members:
  Member "study on the idle mode capability with los and nlos transmissions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cellular connectivity for uavs network modeling performance analysis and design guidelines")
  Member "microscopic analysis of the uplink interference in fdma small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "promises and caveats of uplink iot ultra dense networks")
  Member "nearest neighbour distance distribution in hard core point processes":
    also connected through other hyperedges:
      (none)
  Member "throughput and coverage for a mixed full and half duplex small cell network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backhaul aware uplink communications in full duplex dbs aided hetnets")
  Member "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come")
  Member "what is the true value of dynamic tdd a mac layer perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what is the optimal network deployment for a fixed density of antennas")
  Member "please lower small cell antenna heights in 5g":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges")
  Member "performance impact of los and nlos transmissions in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mobility in the sky performance and mobility analysis for cellular connected uavs")

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.NI(Networking and Internet Architecture)