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 "efficient 3d aerial base station placement considering users mobility by reinforcement learning"):
  Abstract: This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality of service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For fair comparison between the conventional terrestrial network and the aerial BS assisted one, we keep the total number of BSs similar in both networks. Obtaining the max performance in such networks highly depends on the optimal ultimate placement of the aerial-BS. To this end, we need an algorithm which can rely on more general and realistic assumptions and can decide where to go based on the past experiences. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.

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 "user association and bandwidth allocation for terrestrial and aerial base stations with backhaul considerations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "q learning based aerial base station placement for fairness enhancement in mobile networks")
  Member "association of networked flying platforms with small cells for network centric 5g c ran":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatial configuration of agile wireless networks with drone bss and user in the loop")
  Member "a distributed approach for networked flying platform association with small cells in 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uav communications for 5g and beyond recent advances and future trends")
  Member "backhaul aware robust 3d drone placement in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization")
  Member "drone small cells in the clouds design deployment and performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry approach")

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)