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 "federated learning in the sky joint power allocation and scheduling with uav swarms"):
  Abstract: Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a local FL model based on its collected data and then sends this trained local model to the leading UAV who will aggregate the received models, generate a global FL model, and transmit it to followers over the intra-swarm network. To identify how wireless factors, like fading, transmission delay, and UAV antenna angle deviations resulting from wind and mechanical vibrations, impact the performance of FL, a rigorous convergence analysis for FL is performed. Then, a joint power allocation and scheduling design is proposed to optimize the convergence rate of FL while taking into account the energy consumption during convergence and the delay requirement imposed by the swarm's control system. Simulation results validate the effectiveness of the FL convergence analysis and show that the joint design strategy can reduce the number of communication rounds needed for convergence by as much as 35% compared with the baseline design.

Includes members:
  Member "convergence time optimization for federated learning over wireless networks":
    also connected through other hyperedges:
      (none)
  Member "energy efficient radio resource allocation for federated edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy")
  Member "scheduling policies for federated learning in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis and optimization in privacy preserving federated learning")
  Member "a joint learning and communications framework for federated learning over wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art")
  Member "beyond 5g with uavs foundations of a 3d wireless cellular network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "a tutorial on uavs for wireless networks applications challenges and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "resource allocation for d2d communications underlaying a noma based cellular network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey of rate optimal power domain noma schemes for enabling technologies of future 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.LG(Machine Learning)