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: "large deviations of the interference in the ginibre network model"
  Abstract: Under different assumptions on the distribution of the fading random variables, we derive large deviation estimates for the tail of the interference in a wireless network model whose nodes are placed, over a bounded region of the plane, according to the $\beta$-Ginibre process, $0<\beta\leq 1$. The family of $\beta$-Ginibre processes is formed by determinantal point processes, with different degree of repulsiveness, which converge in law to a homogeneous Poisson process, as $\beta \to 0$. In this sense the Poisson network model may be considered as the limiting uncorrelated case of the $\beta$-Ginibre network model. Our results indicate the existence of two different regimes. When the fading random variables are bounded or Weibull superexponential, large values of the interference are typically originated by the sum of several equivalent interfering contributions due to nodes in the vicinity of the receiver. #R##N#In this case, the tail of the interference has, on the log-scale, the same asymptotic behavior for any value of $0<\beta\le 1$, but it differs (again on a log-scale) from the asymptotic behavior of the tail of the interference in the Poisson network model. #R##N#When the fading random variables are exponential or subexponential, instead, large values of the interference are typically originated by a single dominating interferer node and, on the log-scale, the asymptotic behavior of the tail of the interference is essentially insensitive to the distribution of the nodes. As a consequence, on the log-scale, the asymptotic behavior of the tail of the interference in any $\beta$-Ginibre network model, $0<\beta\le 1$, is the same as in the Poisson network model.

Connected through hyperedge HE-1 (induced by source paper "on interference dynamics in matern networks"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "interference functionals in poisson networks"
    - "cooperative relaying under spatially and temporally correlated interference"
    - "the meta distribution of the sir in poisson bipolar and cellular networks"
    - "the ginibre point process as a model for wireless networks with repulsion"
    - "effect of spatial interference correlation on the performance of maximum ratio combining"
    - "resource optimization in device to device cellular systems using time frequency hopping"
    - "spectrum sharing scheme between cellular users and ad hoc device to device users"

Connected through hyperedge HE-2 (induced by source paper "determinantal thinning of point processes with network learning applications"):
  with members:
    - "statistical modeling and probabilistic analysis of cellular networks with determinantal point processes"

Connected through hyperedge HE-3 (induced by source paper "disruptive events in high density cellular networks"):
  with members:
    - "wireless network signals with moderately correlated shadowing still appear poisson"

Connected through hyperedge HE-4 (induced by source paper "downlink coverage probability in a cellular network with ginibre deployed base stations and nakagami m fading channels"):
  with members:
    - "statistical modeling and probabilistic analysis of cellular networks with determinantal point processes"
    - "sinr based k coverage probability in cellular networks with arbitrary shadowing"
    - "downlink mimo hetnets modeling ordering results and performance analysis"
    - "average rate of downlink heterogeneous cellular networks over generalized fading channels a stochastic geometry approach"
    - "downlink coverage analysis in a heterogeneous cellular network"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-5 (induced by source paper "the ginibre point process as a model for wireless networks with repulsion"):
  with members:
    - "modeling heterogeneous network interference using poisson point processes"
    - "mean interference in hard core 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)