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: "backhaul for low altitude uavs in urban environments"
  Abstract: Unmanned Aerial Vehicles (UAVs) acting as access points in cellular networks require wireless backhauls to the core network. In this paper we employ stochastic geometry to carry out an analysis of the UAV backhaul performance that can be achieved with a network of dedicated ground stations. We provide analytical expressions for the probability of successfully establishing a backhaul and the expected data rate over the backhaul link, given either an LTE or a millimeter-wave backhaul. We demonstrate that increasing the density of the ground station network gives diminishing returns on the performance of the UAV backhaul, and that for an LTE backhaul the ground stations can benefit from being colocated with an existing base station network.

Connected through hyperedge HE-1 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry approach"):
  with members:
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "underlay drone cell for temporary events impact of drone height and aerial channel environments"
    - "performance analysis for practical unmanned aerial vehicle networks with los nlos transmissions"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "a stochastic model for uav networks positioned above demand hotspots in urban environments"
    - "coverage and connectivity analysis of millimeter wave vehicular networks"
    - "modeling and analyzing millimeter wave cellular systems"
    - "massive mimo for communications with drone swarms"

Connected through hyperedge HE-2 (induced by source paper "intelligent uav base station selection in urban environments a supervised learning approach"):
  with members:
    - "performance analysis of mobile cellular connected drones under practical antenna configurations"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "mobility in the sky performance and mobility analysis for cellular connected uavs"
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-3 (induced by source paper "on the 3 d placement of airborne base stations using tethered uavs"):
  with members:
    - "capacity and coverage enhancement using long endurance tethered airborne base stations"
    - "average peak age of information minimization in uav assisted iot networks"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "cellular uav to uav communications"
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "post disaster 4g 5g network rehabilitation using drones solving battery and backhaul issues"

Connected through hyperedge HE-4 (induced by source paper "cellular connectivity for uavs network modeling performance analysis and design guidelines"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "cellular uav to uav communications"
    - "a brief history on the theoretical analysis of dense small cell wireless networks"
    - "key technologies and system trade offs for detection and localization of amateur drones"
    - "supporting uav cellular communications through massive mimo"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "mobile networks connected drones field trials simulations and design insights"
    - "ultra reliable uav communication using altitude and cooperation diversity"

Connected through hyperedge HE-5 (induced by source paper "optimal deployments of uavs with directional antennas for a power efficient coverage"):
  with members:
    - "energy efficient resource allocation in uav enabled mobile edge computing networks"
    - "using quantization to deploy heterogeneous nodes in two tier wireless sensor networks"
    - "energy minimization for wireless communication with rotary wing uav"
    - "quantizers with parameterized distortion measures"
    - "reshaping cellular networks for the sky the major factors and feasibility"
    - "deployment and trajectory optimization for uavs a quantization theory approach"
    - "joint altitude and beamwidth optimization for uav enabled multiuser communications"
    - "sensor deployment with limited communication range in homogeneous and heterogeneous wireless sensor networks"

Connected through hyperedge HE-6 (induced by source paper "capacity and coverage enhancement using long endurance tethered airborne base stations"):
  with members:
    - "backhaul aware placement of a uav bs with bandwidth allocation for user centric operation and profit maximization"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "energy efficient uav communication with trajectory optimization"
    - "fso based vertical backhaul fronthaul framework for 5g wireless networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "a stochastic model for uav networks positioned above demand hotspots in urban environments"):
  with members:
    - "dynamic base station repositioning to improve performance of drone small cells"
    - "coexistence of terrestrial and aerial users in cellular networks"
    - "energy efficient uav communication with trajectory optimization"
    - "enriched k tier hetnet model to enable the analysis of user centric small cell deployments"
    - "the new frontier in ran heterogeneity multi tier drone cells"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "mobile internet of things can uavs provide an energy efficient mobile architecture"
    - "a tractable approach to coverage and rate in cellular networks"

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.NI(Networking and Internet Architecture)