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: "predictive deployment of uav base stations in wireless networks machine learning meets contract theory"
  Abstract: In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary flying base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the downlink traffic demand. Next, to guarantee a truthful information exchange between the BS and UAV operators, using the framework of contract theory, a traffic offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utilities of the overloaded ground BSs are maximized. Simulation and analytical results show that the proposed WEM approach yields a prediction error which is lower than 12%, and compared with a conventional expectation maximization approach, the WEM method yields a significant advantage on the prediction accuracy, as the traffic load in the cellular system becomes spatially uneven. Furthermore, compared with a baseline, event-driven allocation method, the proposed predictive deployment approach enables UAV operators to provide efficient downlink service for hotspot users, and improves the revenues of both the BS and UAV network operators significantly.

Connected through hyperedge HE-1 (induced by source paper "heuristic approach for jointly optimizing feicic and uav locations in multi tier lte advanced public safety hetnet"):
  with members:
    - "performance analysis for practical unmanned aerial vehicle networks with los nlos transmissions"
    - "interference coordination for aerial and terrestrial nodes in three tier lte advanced hetnet"
    - "a survey on legacy and emerging technologies for public safety communications"
    - "coexistence of terrestrial and aerial users in cellular networks"
    - "a stochastic geometry model of backhaul and user coverage in urban uav networks"
    - "optimal transport theory for power efficient deployment of unmanned aerial vehicles"
    - "uav assisted heterogeneous networks for capacity enhancement"
    - "drone small cells in the clouds design deployment and performance 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.IT(Information Theory)