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: "placement optimization of uav mounted mobile base stations"
  Abstract: In terrestrial communication networks without fixed infrastructure, unmanned aerial vehicle-mounted mobile base stations (MBSs) provide an efficient solution to achieve wireless connectivity. This letter aims to minimize the number of MBSs needed to provide wireless coverage for a group of distributed ground terminals (GTs), ensuring that each GT is within the communication range of at least one MBS. We propose a polynomial-time algorithm with successive MBS placement, where the MBSs are placed sequentially starting on the area perimeter of the uncovered GTs along a spiral path toward the center, until all GTs are covered. Numerical results show that the proposed algorithm performs favorably compared with other schemes in terms of the number of required MBSs as well as time complexity.

Connected through hyperedge HE-1 (induced by source paper "analysis of uav communications in cell free massive mimo systems"):
  with members:
    - "scalability aspects of cell free massive mimo"
    - "max min rate of cell free massive mimo uplink with optimal uniform quantization"
    - "ubiquitous cell free massive mimo communications"
    - "cellular uav to uav communications"
    - "making cell free massive mimo competitive with mmse processing and centralized implementation"
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "tutorial on uav a blue sky view on wireless communication"
    - "cellular connected uav uplink association power control and interference coordination"

Connected through hyperedge HE-2 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma"):
  with members:
    - "optimizing non orthogonal multiple access in random access networks"
    - "deep reinforcement learning for minimizing age of information in uav assisted networks"
    - "a game theoretic approach for noma aloha"
    - "reward constrained policy optimization"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "risk sensitive reinforcement learning a constrained optimization viewpoint"
    - "addressing function approximation error in actor critic methods"
    - "accelerated primal dual policy optimization for safe reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "cellular traffic offloading through tethered uav deployment and user association"):
  with members:
    - "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization"
    - "capacity and coverage enhancement using long endurance tethered airborne base stations"
    - "stochastic geometry analysis of hybrid aerial terrestrial networks with mmwave backhauling"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "on the 3 d placement of airborne base stations using tethered uavs"
    - "energy minimization for wireless communication with rotary wing uav"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "a stochastic model for uav networks positioned above demand hotspots in urban environments"

Connected through hyperedge HE-5 (induced by source paper "online maneuver design for uav enabled noma systems via reinforcement learning"):
  with members:
    - "fundamental rate limits of uav enabled multiple access channel with trajectory optimization"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "path design for cellular connected uav with reinforcement learning"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "deep learning in physical layer communications"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"

Connected through hyperedge HE-6 (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"
    - "providing wireless coverage to high rise buildings using uavs"

Connected through hyperedge HE-7 (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-8 (induced by source paper "energy efficient resource allocation in uav enabled mobile edge computing networks"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "uav aided offloading for cellular hotspot"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "stochastic optimization and control framework for 5g network slicing with effective isolation"
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "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.IT(Information Theory)