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 "what is the optimal network deployment for a fixed density of antennas"):
  Abstract: In this paper, we answer a fundamental question: when the total number of antennas per square kilometer is fixed, what is the optimal network deployment? A denser network with a less number of antennas per base station (BS) or the opposite case. To evaluate network performance, we consider a practical network scenario with a fixed antennas density and multiuser multiple-input-multiple-output (MU-MIMO) operations for single-antenna users. The number of antennas in each BS is calculated by dividing the antenna density by the BS density. With the consideration of several practical network models, i.e., pilot contamination, a limited user equipment (UE) density and probabilistic line-of-sight (LoS)/non-line-of-sight (NLoS) path loss model, we evaluate the area spectral efficiency (ASE) performance. From our simulation results, we conclude that there exists an optimal BS density for a certain UE density to maximize the ASE performance when the antenna density is fixed. The intuition is that (i) by densifying the network with more BSs, we can achieve a receive power gain due to the smaller distance between the typical UE and its serving BS; (ii) by installing more antennas in each BS, we can achieve a beamforming gain for UEs using MU-MIMO, although such beamforming gain is degraded by pilot contamination; (iii) thus, a trade-off exists between the receive power gain and the beamforming gain, if we fix the antenna density in the network.

Includes members:
  Member "on the performance of practical ultra dense networks the major and minor factors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges")
  Member "heterogeneous cellular networks with los and nlos transmissions the role of massive mimo and small cells":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mobile edge computing enabled heterogeneous networks")
  Member "study on the idle mode capability with los and nlos transmissions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cellular connectivity for uavs network modeling performance analysis and design guidelines")
  Member "what is the true value of dynamic tdd a mac layer perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the major and minor factors in the performance analysis of ultra dense networks")
  Member "please lower small cell antenna heights in 5g":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges")
  Member "performance impact of idle mode capability on dense small cell networks":
    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 idle mode capability on dense small cell networks with los and nlos transmissions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "promises and caveats of uplink iot ultra dense networks")
  Member "performance impact of los and nlos transmissions in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)