human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "stochastic geometry analysis of ultra dense networks impact of antenna height and performance limits"):
  with members:
    - "on the performance of practical ultra dense networks the major and minor factors"
    - "performance impact of base station antenna heights in dense cellular networks"
    - "coverage and capacity scaling laws in downlink ultra dense cellular networks"
    - "please lower small cell antenna heights in 5g"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "are we approaching the fundamental limits of wireless network densification"
    - "sinr and throughput scaling in ultradense urban cellular networks"
    - "downlink cellular network analysis with multi slope path loss models"

Connected through hyperedge HE-2 (induced by source paper "miso in ultra dense networks balancing the tradeoff between user and system performance"):
  with members:
    - "effect of densification on cellular network performance with bounded pathloss model"
    - "performance impact of base station antenna heights in dense cellular networks"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "sinr and throughput scaling in ultradense urban cellular networks"
    - "downlink cellular network analysis with multi slope path loss models"
    - "a stochastic geometry framework for los nlos propagation in dense small cell networks"

Question: Please tell me which class the center node 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)