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 "small cell association with networked flying platforms novel algorithms and performance bounds"):
  Abstract: Fifth generation (5G) and beyond-5G (B5G) systems expect coverage and capacity enhancements along with the consideration of limited power, cost and spectrum. Densification of small cells (SCs) is a promising approach to cater these demands of 5G and B5G systems. However, such an ultra dense network of SCs requires provision of smart backhaul and fronthaul networks. In this paper, we employ a scalable idea of using networked flying platforms (NFPs) as aerial hubs to provide fronthaul connectivity to the SCs. We consider the association problem of SCs and NFPs in a SC network and study the effect of practical constraints related to the system and NFPs. Mainly, we show that the association problem is related to the generalized assignment problem (GAP). Using this relation with the GAP, we show the NP-hard complexity of the association problem and further derive an upper bound for the maximum achievable sum data rate. Linear Programming relaxation of the problem is also studied to compare the results with the derived bounds. Finally, two efficient (less complex) greedy solutions of the association problem are presented, where one of them is a distributed solution and the other one is its centralized version. Numerical results show a favorable performance of the presented algorithms with respect to the exhaustive search and derived bounds. The computational complexity comparison of the algorithms with the exhaustive search is also presented to show that the presented algorithms can be practically implemented.

Includes members:
  Member "on the number and 3d placement of drone base stations in wireless cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "air to air communications beyond 5g a novel 3d comp transmission scheme")
  Member "user association and bandwidth allocation for terrestrial and aerial base stations with backhaul considerations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization")
  Member "optimal transport theory for cell association in uav enabled cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "q learning based aerial base station placement for fairness enhancement in mobile networks")
  Member "a novel airborne self organising architecture for 5g networks":
    also connected through other hyperedges:
      (none)
  Member "backhaul aware robust 3d drone placement in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization")
  Member "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "fso based vertical backhaul fronthaul framework for 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cellular traffic offloading through tethered uav deployment and user association")

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)