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 "predictive deployment of uav base stations in wireless networks machine learning meets contract theory"):
  Abstract: In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary flying base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the downlink traffic demand. Next, to guarantee a truthful information exchange between the BS and UAV operators, using the framework of contract theory, a traffic offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utilities of the overloaded ground BSs are maximized. Simulation and analytical results show that the proposed WEM approach yields a prediction error which is lower than 12%, and compared with a conventional expectation maximization approach, the WEM method yields a significant advantage on the prediction accuracy, as the traffic load in the cellular system becomes spatially uneven. Furthermore, compared with a baseline, event-driven allocation method, the proposed predictive deployment approach enables UAV operators to provide efficient downlink service for hotspot users, and improves the revenues of both the BS and UAV network operators significantly.

Includes members:
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "beyond 5g with uavs foundations of a 3d wireless cellular network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "on the number and 3d placement of drone base stations in wireless cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient resource allocation in uav enabled mobile edge computing networks")
  Member "uav aided offloading for cellular hotspot":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d uav trajectory and communication design for simultaneous uplink and downlink transmission")
  Member "energy minimization for wireless communication with rotary wing uav":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "on demand deployment of multiple aerial base stations for traffic offloading and network recovery":
    also connected through other hyperedges:
      (none)
  Member "a tutorial on uavs for wireless networks applications challenges and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")

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)