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 "stochastic geometry analysis of ultra dense networks impact of antenna height and performance limits"):
  Abstract: The concept of Ultra Dense Networks (UDN) is often seen as a key enabler of the next generation mobile networks. However, existing analysis of UDNs, including Stochastic Geometry, has not been able to fully determine the potential gains and limits of densification. In this paper we study performance of UDNs in downlink and provide new insights on the impact of antenna height and careful site selection on the network performance. We focus our investigation on the probability of coverage, average cell rate and average area spectral efficiency for networks with regular and random BS deployments. We show that under a path-loss model which considers antenna height there exists an upper limit on network performance which is dependent on the path-loss model parameters. Our analysis shows an interesting finding that even for over-densified networks a non-negligible system performance can be achieved.

Includes members:
  Member "the major and minor factors in the performance analysis of ultra dense networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "miso in ultra dense networks balancing the tradeoff between user and system performance")
  Member "on the performance of practical ultra dense networks the major and minor factors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges")
  Member "performance impact of base station antenna heights in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "coverage and capacity scaling laws in downlink ultra dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "downlink cellular network analysis with los nlos propagation and elevated base stations")
  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 los and nlos transmissions in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mobility in the sky performance and mobility analysis for cellular connected uavs")
  Member "are we approaching the fundamental limits of wireless network densification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "resource management in wireless networks via multi agent deep reinforcement learning")
  Member "sinr and throughput scaling in ultradense urban cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sum rate maximization of multicell miso networks with limited information exchange")

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)