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 "radio map based 3d path planning for cellular connected uav"):
  Abstract: In this paper, we study the three-dimensional (3D) path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the expected signal-to-interference-plus-noise ratio (SINR) at the UAV receiver with each of its associated ground base stations (GBSs) during the flight. To exploit the location-dependent and spatially varying channel and interference over the air, we propose a new radio map based path planning for the UAV. Specifically, we first utilize the channel gain map of each GBS that provides its large-scale channel gains with uniformly sampled locations on a 3D grid over the region of interest, which are due to fixed and large-size obstacles and thus assumed to be time-invariant during the UAV's flight. Based on the channel gain maps of the GBSs and their loading factors, we construct an SINR map that depicts the expected SINR levels over the sampled 3D locations. By leveraging the obtained SINR map, we then derive the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. To reduce the computational complexity, we further propose a grid quantization method whereby the grid points in the SINR map are more coarsely sampled by exploiting the spatial channel/interference correlation over neighboring grids. Then, we solve an approximate SPP over the reduced-size SINR map more efficiently. Numerical results show that the proposed solutions can effectively minimize the UAV flying distance subject to its communication quality constraint, and a flexible trade-off between performance and complexity can be achieved by adjusting the quantization ratio in the SINR map. Moreover, the proposed solution significantly outperforms various benchmark schemes without fully exploiting the channel/interference spatial distribution in the network.

Includes members:
  Member "trajectory design for cellular connected uav under outage duration constraint":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a double q learning approach for navigation of aerial vehicles with connectivity constraint")
  Member "interference management for cellular connected uavs a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  Member "connectivity aware uav path planning with aerial coverage maps":
    also connected through other hyperedges:
      (none)
  Member "hybrid offline online design for uav enabled data harvesting in probabilistic los channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fundamental rate limits of uav enabled multiple access channel with trajectory optimization")
  Member "3d trajectory optimization in rician fading for uav enabled data harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d uav trajectory and communication design for simultaneous uplink and downlink transmission")
  Member "trajectory optimization for cellular connected uav under outage duration constraint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mobility in the sky performance and mobility analysis for cellular connected uavs")
  Member "cognitive uav communication via joint maneuver and power control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "comp in the sky uav placement and movement optimization for multi user communications":
    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")

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)