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 stochastic geometry analyses of cellular networks with alpha stable self similarity"):
  Abstract: To understand the spatial deployment of base stations (BSs) is the first step to facilitate the performance analyses of cellular networks, as well as the design of efficient networking protocols. Poisson point process (PPP) has been widely adopted to characterize the deployment of BSs and established the reputation to give tractable results in the stochastic geometry analyses. However, given the total randomness assumption in PPP, its accuracy has been recently questioned. On the other hand, the actual deployment of BSs during the past long evolution is highly correlated with heavy-tailed human activities. The {\alpha}-stable distribution, one kind of heavy-tailed distributions, has demonstrated superior accuracy to statistically model the spatial density of BSs. In this paper, we start with the new findings on {\alpha}-stable distributed BSs deployment and investigate the intrinsic feature (i.e., the spatial self-similarity) embedded in the BSs. Based on these findings, we perform the coverage probability analyses and provide the relevant theoretical results. In particular, we show that for some special cases, our work could reduce to the fundamental work by J. G. Andrews. We also examine the network performance under extensive simulation settings and validate that the simulations results are consistent with our theoretical derivations.

Includes members:
  Member "system level modeling and optimization of the energy efficiency in cellular networks a stochastic geometry framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come")
  Member "delivery secrecy tradeoff for cache enabled stochastic networks content placement optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "enhancing physical layer security of random caching in large scale multi antenna heterogeneous wireless networks")
  Member "self interference in full duplex multi user mimo channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "full duplex cloud radio access network stochastic design and analysis")
  Member "secrecy outage analysis for downlink transmissions in the presence of randomly located eavesdroppers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive full duplex jamming receiver for secure d2d links in random networks")
  Member "modeling and analysis of cellular networks using stochastic geometry a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "analytical modeling of interference aware power control for the uplink of heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a spatiotemporal model for peak aoi in uplink iot networks time vs event triggered traffic")
  Member "analysis of massive mimo enabled downlink wireless backhauling for full duplex small cells":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "millimeter wave integrated access and backhaul in 5g performance analysis and design insights")
  Member "the learning and prediction of application level traffic data in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effect of spatial and temporal traffic statistics on the performance of wireless 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)