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: "on the number and 3d placement of drone base stations in wireless cellular networks"
  Abstract: Using drone base stations (drone-BSs) in wireless networks has started attracting attention. Drone-BSs can assist the ground BSs in both capacity and coverage enhancement. One of the important problems about integrating drone-BSs to cellular networks is the management of their placement to satisfy the dynamic system requirements. In this paper, we propose a method to find the positions of drone-BSs in an area with different user densities using a heuristic algorithm. The goal is to find the minimum number of drone-BSs and their 3D placement so that all the users are served. Our simulation results show that the proposed approach can satisfy the quality-of-service requirements of the network.

Connected through hyperedge HE-1 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception"):
  with members:
    - "efficient local map search algorithms for the placement of flying relays"
    - "secrecy transmission in large scale uav enabled wireless networks"
    - "cognitive uav communication via joint maneuver and power control"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "securing uav communications via joint trajectory and power control"
    - "learning to communicate in uav aided wireless networks map based approaches"

Connected through hyperedge HE-2 (induced by source paper "voting based multi agent reinforcement learning for intelligent iot"):
  with members:
    - "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "placement optimization of uav mounted mobile base stations"

Connected through hyperedge HE-3 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization"):
  with members:
    - "uav base station location optimization for next generation wireless networks overview and future research directions"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "spatial configuration of agile wireless networks with drone bss and user in the loop"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "user association and bandwidth allocation for terrestrial and aerial base stations with backhaul considerations"
    - "backhaul aware robust 3d drone placement in 5g wireless networks"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-4 (induced by source paper "distributed collaborative 3d deployment of uav base stations for on demand coverage"):
  with members:
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "the new frontier in ran heterogeneity multi tier drone cells"
    - "drone small cells in the clouds design deployment and performance analysis"

Connected through hyperedge HE-5 (induced by source paper "multi purpose aerial drones for network coverage and package delivery"):
  with members:
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "uav aided offloading for cellular hotspot"
    - "on the min cost traveling salesman problem with drone"
    - "spatial configuration of agile wireless networks with drone bss and user in the loop"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "joint trajectory and communication design for uav enabled multiple access"

Connected through hyperedge HE-6 (induced by source paper "q learning based aerial base station placement for fairness enhancement in mobile networks"):
  with members:
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "placement optimization of uav mounted mobile base stations"
    - "optimal transport theory for cell association in uav enabled cellular networks"
    - "coexistence of terrestrial and aerial users in cellular networks"
    - "backhaul aware robust 3d drone placement in 5g wireless networks"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-7 (induced by source paper "3d multi drone cell trajectory design for efficient iot data collection"):
  with members:
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "evaluating the performance of emtc and nb iot for smart city applications"
    - "backhaul aware robust 3d drone placement in 5g wireless networks"
    - "energy efficient uav communication with trajectory optimization"

Connected through hyperedge HE-8 (induced by source paper "communications and networking technologies for intelligent drone cruisers"):
  with members:
    - "a predictive on demand placement of uav base stations using echo state network"
    - "on demand density aware uav base station 3d placement for arbitrarily distributed users with guaranteed data rates"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "placement optimization of uav mounted mobile base stations"
    - "multi scale context aggregation by dilated convolutions"

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)