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: "coverage gain and device to device user density stochastic geometry modeling and analysis"
  Abstract: Device-to-device (D2D) communication has huge potential for capacity and coverage enhancements for next generation cellular networks. The number of potential nodes for D2D communication is an important parameter that directly impacts the system capacity. In this letter, we derive an analytic expression for average coverage probability of cellular user and corresponding number of potential D2D users. In this context, mature framework of stochastic geometry and Poisson point process have been used. The retention probability has been incorporated in Laplace functional to capture reduced path-loss and shortest distance criterion based D2D pairing. The numerical results show a close match between analytic expression and simulation setup.

Connected through hyperedge HE-1 (induced by source paper "spatial and social paradigms for interference and coverage analysis in underlay d2d network"):
  with members:
    - "capacity of cellular networks with femtocache"
    - "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis"
    - "sinr based k coverage probability in cellular networks with arbitrary shadowing"
    - "power control for d2d underlaid cellular networks modeling algorithms and analysis"
    - "analytical modeling of uplink cellular networks"
    - "modeling heterogeneous network interference using poisson point processes"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-2 (induced by source paper "intracell interference characterization and cluster inference for d2d communication"):
  with members:
    - "statistical modeling and probabilistic analysis of cellular networks with determinantal point processes"
    - "the ginibre point process as a model for wireless networks with repulsion"
    - "modeling heterogeneous network interference using poisson point processes"
    - "modeling and analysis of k tier downlink heterogeneous cellular 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)