human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "3d map assisted uav trajectory design under cellular connectivity constraints"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "path design for cellular connected uav with reinforcement learning"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "5g 3gpp like channel models for outdoor urban microcellular and macrocellular environments"

Question: Please tell me which class the center node 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)