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: "blocking probability and spatial throughput characterization for cellular enabled uav network with directional antenna"
  Abstract: The past few years have witnessed a tremendous increase on the use of unmanned aerial vehicles (UAVs) in civilian applications, which call for high-performance communication between UAVs and their ground clients, especially when they are densely deployed. To achieve this goal, cellular base stations (BSs) can be leveraged to provide a new and promising solution to support massive UAV communications simultaneously in a cost-effective way. However, different from terrestrial communication channels, UAV-to-BS channels are usually dominated by the light-of-sight (LoS) link, which aggravates the co-channel interference and renders the spatial frequency reuse in existing cellular networks ineffective. In this paper, we consider the use of a directional antenna at each UAV to confine the interference to/from other UAV users within a limited region and hence improve the spatial reuse of the spectrum. Under this model, a UAV user may be temporarily blocked from communication if it cannot find any BS in its antenna main-lobe, or it finds that all BSs under its main-lobe are simultaneously covered by those of some other UAVs and hence suffer from strong co-channel interference. Assuming independent homogeneous Poisson point processes (HPPPs) for the UAVs' and ground BSs' locations respectively, we first analytically derive a closed-form upper bound for the UAV blocking probability and then characterize the achievable average spatial throughput of the cellular-enabled UAV communication network, in terms of various key parameters including the BS/UAV densities as well as the UAV's flying altitude and antenna beamwidth. Simulation results verify that the derived bound is practically tight, and further show that adaptively adjusting the UAV altitude and/or beamwidth with different BS/UAV densities can significantly reduce the UAV blocking probability and hence improve the network spatial throughput.

Connected through hyperedge HE-1 (induced by source paper "dynamic model for network selection in next generation hetnets with memory affecting rational users"):
  with members:
    - "a learning based coexistence mechanism for laa lte based hetnets"
    - "energy minimization for wireless communication with rotary wing uav"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "green 5g heterogeneous networks through dynamic small cell operation"
    - "physical layer security in millimeter wave cellular networks"
    - "5g ultra dense cellular networks"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "what will 5g be"

Connected through hyperedge HE-2 (induced by source paper "how do non ideal uav antennas affect air to ground communications"):
  with members:
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "an overview of 3gpp release 15 study on enhanced lte support for connected drones"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "multi tier drone architecture for 5g b5g cellular networks challenges trends and prospects"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"

Connected through hyperedge HE-3 (induced by source paper "beyond 5g with uavs foundations of a 3d wireless cellular network"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "uav aided offloading for cellular hotspot"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "uav enabled aerial base station bs iii iii capacity characterization of uav enabled two user broadcast channel"
    - "common throughput maximization in uav enabled ofdma systems with delay consideration"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"

Connected through hyperedge HE-4 (induced by source paper "energy optimization for cellular connected multi uav mobile edge computing systems with multi access schemes"):
  with members:
    - "fundamental tradeoffs in communication and trajectory design for uav enabled wireless network"
    - "securing uav communications via joint trajectory and power control"
    - "spectral and energy efficient wireless powered iot networks noma or tdma"
    - "energy minimization for wireless communication with rotary wing uav"
    - "mobile edge computing for cellular connected uav computation offloading and trajectory optimization"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "uav enabled aerial base station bs iii iii capacity characterization of uav enabled two user broadcast channel"

Connected through hyperedge HE-5 (induced by source paper "joint configuration of transmission direction and altitude in uav based two way communication"):
  with members:
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "interference spins scheduling of multiple interfering two way wireless links"

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)