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: "massive uav to ground communication and its stable movement control a mean field approach"
  Abstract: This paper proposes a real-time movement control algorithm for massive unmanned aerial vehicles (UAVs) that provide emergency cellular connections in an urban disaster site. While avoiding the inter-UAV collision under temporal wind dynamics, the proposed algorithm minimizes each UAV's energy consumption per unit downlink rate. By means of a mean-field game theoretic flocking approach, the velocity control of each UAV only requires its own location and channel states. Numerical results validate the performance of the algorithm in terms of the number of collision and energy consumption per data rate, under a realistic 3GPP UAV channel model.

Connected through hyperedge HE-1 (induced by source paper "communication efficient massive uav online path control federated learning meets mean field game theory"):
  with members:
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "remote uav online path planning via neural network based opportunistic control"
    - "reinforcement learning for uav attitude control"
    - "autonomous uav navigation using reinforcement learning"
    - "virtualized control over fog interplay between reliability and latency"
    - "energy efficient uav communication with trajectory optimization"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "sampling for remote estimation through queues age of information and beyond"):
  with members:
    - "age optimal updates of multiple information flows"
    - "tutorial on uav a blue sky view on wireless communication"
    - "information aging through queues a mutual information perspective"
    - "remote estimation over a packet drop channel with markovian state"
    - "a dynamic jamming game for real time status updates"
    - "joint status sampling and updating for minimizing age of information in the internet of things"
    - "quantifying link stability in ad hoc wireless networks subject to ornstein uhlenbeck mobility"
    - "optimizing information freshness in wireless networks under general interference constraints"

Connected through hyperedge HE-3 (induced by source paper "remote uav online path planning via neural network based opportunistic control"):
  with members:
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "wireless network intelligence at the edge"
    - "autonomous uav navigation using reinforcement learning"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-4 (induced by source paper "massive autonomous uav path planning a neural network based mean field game theoretic approach"):
  with members:
    - "wireless network intelligence at the edge"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "virtualized control over fog interplay between reliability and latency"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-5 (induced by source paper "quantifying link stability in ad hoc wireless networks subject to ornstein uhlenbeck mobility"):
  with members:
    - "entropy rate of time varying wireless networks"
    - "on the distribution of random geometric graphs"

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)