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 "optimized trajectory design in uav based cellular networks for 3d users a double q learning approach"):
  Abstract: In this paper, the problem of trajectory design of unmanned aerial vehicles (UAVs) for maximizing the number of satisfied users is studied in a UAV based cellular network where the UAV works as a flying base station that serves users, and the user indicates its satisfaction in terms of completion of its data request within an allowable maximum waiting time. The trajectory design is formulated as an optimization problem whose goal is to maximize the number of satisfied users. To solve this problem, a machine learning framework based on double Q-learning algorithm is proposed. The algorithm enables the UAV to find the optimal trajectory that maximizes the number of satisfied users. Compared to the traditional learning algorithms, such as Q-learning that selects and evaluates the action using the same Q-table, the proposed algorithm can decouple the selection from the evaluation, therefore avoid overestimation which leads to sub-optimal policies. Simulation results show that the proposed algorithm can achieve up to 19.4% and 14.1% gains in terms of the number of satisfied users compared to random algorithm and Q-learning algorithm.

Includes members:
  Member "interference management for cellular connected uavs a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  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-2 (induced by source paper "energy efficient resource allocation in uav enabled mobile edge computing networks")
  Member "joint altitude beamwidth location and bandwidth optimization for uav enabled communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource allocation for secure multi uav communication systems with multi eavesdropper")
  Member "joint trajectory and communication design for multi uav enabled wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "virtual reality over wireless networks quality of service model and learning based resource management":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces")
  Member "artificial neural networks based machine learning for wireless networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "uav enabled wireless power transfer trajectory design and energy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the age of information in internet of things systems with correlated devices")

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)