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: "the learning and prediction of application level traffic data in cellular networks"
  Abstract: Traffic learning and prediction is at the heart of the evaluation of the performance of telecommunications networks and attracts a lot of attention in wired broadband networks. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In this paper, we firstly collect a significant amount of application-level traffic data from cellular network operators. Afterwards, with the aid of the traffic "big data", we make a comprehensive study over the modeling and prediction framework of cellular network traffic. Our results solidly demonstrate that there universally exist some traffic statistical modeling characteristics, including ALPHA-stable modeled property in the temporal domain and the sparsity in the spatial domain. Meanwhile, the results also demonstrate the distinctions originated from the uniqueness of different service types of applications. Furthermore, we propose a new traffic prediction framework to encompass and explore these aforementioned characteristics and then develop a dictionary learning-based alternating direction method to solve it. Besides, we validate the prediction accuracy improvement and the robustness of the proposed framework through extensive simulation results.

Connected through hyperedge HE-1 (induced by source paper "effect of spatial and temporal traffic statistics on the performance of wireless networks"):
  with members:
    - "tradeoff between delay and physical layer security in wireless networks"
    - "the meta distribution of the sir for cellular networks with power control"
    - "stable throughput and delay analysis of a random access network with queue aware transmission"
    - "random access analysis for massive iot networks under a new spatio temporal model a stochastic geometry approach"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "poisson cluster process based analysis of hetnets with correlated user and base station locations"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "user centric mobility management in ultra dense cellular networks under spatio temporal dynamics"

Connected through hyperedge HE-2 (induced by source paper "predictive deployment of uav base stations in wireless networks machine learning meets contract theory"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "uav aided offloading for cellular hotspot"
    - "energy minimization for wireless communication with rotary wing uav"
    - "on demand deployment of multiple aerial base stations for traffic offloading and network recovery"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"

Connected through hyperedge HE-3 (induced by source paper "the stochastic geometry analyses of cellular networks with alpha stable self similarity"):
  with members:
    - "system level modeling and optimization of the energy efficiency in cellular networks a stochastic geometry framework"
    - "delivery secrecy tradeoff for cache enabled stochastic networks content placement optimization"
    - "self interference in full duplex multi user mimo channels"
    - "secrecy outage analysis for downlink transmissions in the presence of randomly located eavesdroppers"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "analytical modeling of interference aware power control for the uplink of heterogeneous cellular networks"
    - "analysis of massive mimo enabled downlink wireless backhauling for full duplex small cells"
    - "opportunistic jamming for enhancing security stochastic geometry modeling and analysis"

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)