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: "left inverses of fractional laplacian and sparse stochastic processes"
  Abstract: The fractional Laplacian $(-\triangle)^{\gamma/2}$ commutes with the primary coordination transformations in the Euclidean space $\RR^d$: dilation, translation and rotation, and has tight link to splines, fractals and stable Levy processes. For $0<\gamma<d$, its inverse is the classical Riesz potential $I_\gamma$ which is dilation-invariant and translation-invariant. In this work, we investigate the functional properties (continuity, decay and invertibility) of an extended class of differential operators that share those invariance properties. In particular, we extend the definition of the classical Riesz potential $I_\gamma$ to any non-integer number $\gamma$ larger than $d$ and show that it is the unique left-inverse of the fractional Laplacian $(-\triangle)^{\gamma/2}$ which is dilation-invariant and translation-invariant. We observe that, for any $1\le p\le \infty$ and $\gamma\ge d(1-1/p)$, there exists a Schwartz function $f$ such that $I_\gamma f$ is not $p$-integrable. We then introduce the new unique left-inverse $I_{\gamma, p}$ of the fractional Laplacian $(-\triangle)^{\gamma/2}$ with the property that $I_{\gamma, p}$ is dilation-invariant (but not translation-invariant) and that $I_{\gamma, p}f$ is $p$-integrable for any Schwartz function $f$. We finally apply that linear operator $I_{\gamma, p}$ with $p=1$ to solve the stochastic partial differential equation $(-\triangle)^{\gamma/2} \Phi=w$ with white Poisson noise as its driving term $w$.

Connected through hyperedge HE-1 (induced by source paper "sparse stochastic processes and discretization of linear inverse problems"):
  with members:
    - "bayesian estimation for continuous time sparse stochastic processes"
    - "a unified formulation of gaussian vs sparse stochastic processes part ii discrete domain theory"
    - "a unified formulation of gaussian vs sparse stochastic processes part i continuous domain theory"

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)