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: "local large deviation principle large deviation principle for the signal to interference and noise ratio graph models"
  Abstract: Given devices space $D$, an intensity measure $\lambda\in(0,\infty)$, transition kernel from the positive reals to the space $D,$ a path -loss function which depends on some positive constant $\alpha,$ and some technical constants $\tau_{\lambda},\gamma_{\lambda}:(0,\,\infty)\to (0,\infty)$ we define a Marked Poisson Point Process(MPPP) and, a Signal-to- Interference and Noise Ratio (SINR) graph model. For the SINR graph model we define the \emph{empirical marked measure} and the \emph{empirical connectivity measure}. For a class of SINR graphs model, we prove joint Large Deviation Principle(LDP) for the empirical marked measure and the empirical connectivity measure with speed $\lambda$ in the $\tau$-topology. In particular if $D=\R^{d-2}\times[-T,\,T]^2,$ for $T>0,$ we obtain a much explicit expression for the rate function. From the joint large deviation principle we obtain an Asymptotic Equipartition Property(AEP) for network structured data modelled as an SINR graph. In particular if $D=\R^{d-2}\times[-T,\,T]^2,$ for $T>0,$ we obtain a explicit function. We also prove a Local Large Deviation Principle(LLDP) for the class of SINR graphs on $D=\R^{d-2}\times[-T,\,T]^2,$ with speed $\lambda$ from \emph{ spectral potential} point. Given, an empirical marked measure $\omega$, we define the so-called spectral potential $U_{R_{T,d}}(\omega,\,\cdot),$ for the SINR graph process, where $R_{T,d}$ is a properly defined constant function which depends on the device locations and the marks. We show that the \emph{Kullback action} or the \emph{ divergence function} $I_{\omega}(\pi),$ with respect to the empirical connectivity measure $\pi,$ is the legendre dual of the spectral potential.

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)