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: "empirical chaos processes and blind deconvolution"
  Abstract: This paper investigates conditions under which certain kinds of systems of bilinear equations have a unique structured solution. In particular, we look at when we can recover vectors $\boldsymbol{w},\boldsymbol{q}$ from observations of the form \[ y_{\ell} =   , \quad \ell = 1,\ldots,L, \] where $\boldsymbol{b}_\ell,\boldsymbol{c}_\ell$ are known. We show that if $\boldsymbol{w}\in\mathbb{C}^{M_1}$ and $\boldsymbol{q}\in\mathbb{C}^{M_2}$ are sparse, with no more than $K$ and $N$ nonzero entries, respectively, and the $\boldsymbol{b}_\ell,\boldsymbol{c}_\ell$ are generic, selected as independent Gaussian random vectors, then $\boldsymbol{w},\boldsymbol{q}$ are uniquely determined from \[ #R##N#L \geq \mathrm{Const}\cdot (K+N)\log^5(M_1M_2) \] such equations with high probability. #R##N#The key ingredient in our analysis is a uniform probabilistic bound on how far a random process of the form \[Z(\boldsymbol{X}) = \sum_{\ell=1}^L|\boldsymbol{b}_\ell^*\boldsymbol{X}\boldsymbol{c}_\ell|^2 \] deviates from its mean over a set of structured matrices $\boldsymbol{X}\in\mathcal{X}$. As both $\boldsymbol{b}_\ell$ and $\boldsymbol{c}_\ell$ are random, this is a specialized type of $4$th order chaos; we refer to $Z(\boldsymbol{X})$ as an {\em empirical chaos process}. Bounding this process yields a set of general conditions for when the map $\boldsymbol{X}\rightarrow \{\boldsymbol{b}_\ell^*\boldsymbol{X}\boldsymbol{c}_\ell\}_{\ell=1}^L$ is a restricted isometry over the set of matrices $\mathcal{X}$. The conditions are stated in terms of general geometric properties of the set $\mathcal{X}$, and are explicitly computed for the case where $\mathcal{X}$ is the set of matrices that are simultaneously sparse and low rank.

Connected through hyperedge HE-1 (induced by source paper "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "low complexity multiclass encryption by compressed sensing"
    - "blind sensor calibration using approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "self calibration and bilinear inverse problems via linear least squares"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "leveraging diversity and sparsity in blind deconvolution"
    - "optimal sample complexity for blind gain and phase calibration"
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"

Connected through hyperedge HE-3 (induced by source paper "through the haze a non convex approach to blind gain calibration for linear random sensing models"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "a geometric analysis of phase retrieval"
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind sensor calibration using approximate message passing"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"

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)