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 placement of a uav bs with bandwidth allocation for user centric operation and profit maximization"
  Abstract: Addressing the Quality-of-Service (QoS) requirements of users is crucial for service providers to improve the network performance. Furthermore, the transformation from the network-centric to user-centric service paradigm requires service providers to focus on improving the Quality-of-Experience (QoE) which is expected to become an important objective in Next Generation Cellular Networks (NGCNs). Managing QoE is not only a technical issue but also a marketing ability to improve profitability. An efficient strategy to improve the profitability is to apply price differentiation for different service levels. Unmanned Aerial Vehicle Base Stations (UAV-BSs) are envisioned to be an integral component of NGCNs and they create opportunities to enhance the capacity of the network by dynamically moving the supply towards the demand while facilitating services that cannot be provided via other means efficiently. However, building a reliable wireless backhaul link via optimized resource allocation is a key issue for the placement of UAV-BSs. In this paper, we consider a UAV-BS and a terrestrial network of Macro-cell Base Stations (MBSs) that the UAV-BS rely on for backhauling. The problem is to determine the 3D location of the UAV-BS and the bandwidth allocations to each user to maximize the profitability of service provided in terms of achievable data rate levels. We develop a Mixed Integer Non-Linear Programming (MINLP) formulation of the problem. To overcome the high complexity, we propose a novel search algorithm that is very efficient in terms of solution quality and time. The analysis performed through numerical evaluations reveal that offering multiple data rate options to users improves the QoE and at the same time allows the service providers to increase the total profit.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"

Connected through hyperedge HE-3 (induced by source paper "capacity and coverage enhancement using long endurance tethered airborne base stations"):
  with members:
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "backhaul for low altitude uavs in urban environments"
    - "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-4 (induced by source paper "uav base station location optimization for next generation wireless networks overview and future research directions"):
  with members:
    - "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"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "spatial configuration of agile wireless networks with drone bss and user in the loop"
    - "efficient 3d placement of a uav using particle swarm optimization"
    - "placement optimization of uav mounted mobile base stations"
    - "providing wireless coverage to high rise buildings using uavs"
    - "joint trajectory and communication design for multi uav enabled wireless 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)