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 aware robust 3d drone placement in 5g wireless networks"
  Abstract: Using drones as flying base stations is a promising approach to enhance the network coverage and area capacity by moving supply towards demand when required. However deployment of such base stations can face some restrictions that need to be considered. One of the limitations in drone base stations (drone-BSs) deployment is the availability of reliable wireless backhaul link. This paper investigates how different types of wireless backhaul offering various data rates would affect the number of served users. Two approaches, namely, network-centric and user-centric, are introduced and the optimal 3D backhaul-aware placement of a drone-BS is found for each approach. To this end, the total number of served users and sum-rates are maximized in the network-centric and user-centric frameworks, respectively. Moreover, as it is preferred to decrease drone-BS movements to save more on battery and increase flight time and to reduce the channel variations, the robustness of the network is examined as how sensitive it is with respect to the users displacements.

Connected through hyperedge HE-1 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization"):
  with members:
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "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"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-2 (induced by source paper "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"):
  with members:
    - "resource allocation for solar powered uav communication systems"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "indoor mobile coverage problem using uavs"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "efficient 3d placement of a uav using particle swarm optimization"
    - "placement optimization of uav mounted mobile base stations"

Connected through hyperedge HE-3 (induced by source paper "joint access and backhaul resource management in satellite drone networks a competitive market approach"):
  with members:
    - "exploiting noma for uav communications in large scale cellular networks"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "backhaul aware placement of a uav bs with bandwidth allocation for user centric operation and profit maximization"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "beam tracking for uav mounted satcom on the move with massive antenna array"
    - "user association and bandwidth allocation for terrestrial and aerial base stations with backhaul considerations"

Connected through hyperedge HE-4 (induced by source paper "q learning based aerial base station placement for fairness enhancement in mobile networks"):
  with members:
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "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"
    - "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-5 (induced by source paper "3d multi drone cell trajectory design for efficient iot data collection"):
  with members:
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "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"
    - "energy efficient uav communication with trajectory optimization"

Connected through hyperedge HE-6 (induced by source paper "on demand density aware uav base station 3d placement for arbitrarily distributed users with guaranteed data rates"):
  with members:
    - "placement optimization of uav mounted mobile base stations"
    - "energy efficient 3d uav bs placement versus mobile users density and circuit power"

Connected through hyperedge HE-7 (induced by source paper "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "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"
    - "energy minimization for wireless communication with rotary wing uav"

Connected through hyperedge HE-8 (induced by source paper "design challenges of multi uav systems in cyber physical applications a comprehensive survey and future directions"):
  with members:
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "oil observational imitation learning"
    - "uplink coverage performance of an underlay drone cell for temporary events"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "ultra low power deep learning powered autonomous nano drones"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "how hard is it to cross the room training recurrent neural networks to steer a uav"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"

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)