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 "system level analysis of heterogeneous networks under imperfect traffic hotspot localization"):
  Abstract: We study, in this paper, the impact of imperfect small cell positioning with respect to traffic hotspots in cellular networks. In order to derive the throughput distribution in macro and small cells, we firstly perform static level analysis of the system considering a non-uniform distribution of user locations. We secondly introduce the dynamics of the system, characterized by random arrivals and departures of users after a finite service duration, with the service rates and distribution of radio conditions outfitted from the first part of the work. When dealing with the dynamics of the system, macro and small cells are modeled by multi-class processor sharing queues. Macro and small cells are assumed to be operating in the same bandwidth. Consequently, they are coupled due to the mutual interferences generated by each cell to the other. We derive several performance metrics such as the mean flow throughput and the gain, if any, generated from deploying small cells to manage traffic hotspots. Our results show that in case the hotspot is near the macro BS (Base Station), even a perfect positioning of the small cell will not yield improved performance due to the high interference experienced at macro and small cell users. However, in case the hotspot is located far enough from the macro BS, performing errors in small cell positioning is tolerated (since related results show positive gains) and it is still beneficial in offloading traffic from the congested macrocell. The best performance metrics depend also on several other important factors such as the users' arrival intensity, the capacity of the cell and the size of the traffic hotspot

Includes members:
  Member "analytical tractability of hexagonal network model with random user location":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic tdd interference analysis in macro cell and small cell deployments")
  Member "5g wireless backhaul networks challenges and research advances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "spectrum and energy efficiency evaluation of two tier femtocell networks with partially open channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "downlink small cell base station cooperation strategy in fractal small cell networks")
  Member "emerging technologies and research challenges for 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blockchain for 5g and beyond networks a state of the art survey")
  Member "traffic hotspot localization in 3g and 4g wireless networks using omc metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "offloading traffic hotspots using moving small cells")
  Member "modeling heterogeneous network interference using poisson point processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards reliable uav swarm communication in d2d enhanced cellular network")
  Member "modeling and analysis of k tier downlink heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense 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.NI(Networking and Internet Architecture)