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 "hybrid offline online design for uav enabled data harvesting in probabilistic los channel"):
  Abstract: This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs' locations and the probabilistic LoS channel model is known a priori, while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of the complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs' individual amounts of data received accumulatively.

Includes members:
  Member "average peak age of information minimization in uav assisted iot networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "who should google scholar update more often")
  Member "efficient local map search algorithms for the placement of flying relays":
    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 "interference management for cellular connected uavs a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  Member "deep reinforcement learning for minimizing age of information in uav assisted networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma")
  Member "3d trajectory optimization in rician fading for uav enabled data harvesting":
    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 "path design for cellular connected uav with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online maneuver design for uav enabled noma systems via reinforcement learning")
  Member "age optimal trajectory planning for uav assisted data collection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for fresh data collection in uav assisted iot networks")
  Member "local map assisted positioning for flying wireless relays":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "uav positioning and power control for two way wireless relaying")

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)