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: "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
  Abstract: The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing matrix be known a priori. Such an ideal assumption may not be met in practical applications where various errors and fluctuations exist in the sensing instruments. This paper considers the problem of compressed sensing subject to a structured perturbation in the sensing matrix. Under mild conditions, it is shown that a sparse signal can be recovered by l1 minimization and the recovery error is at most proportional to the measurement noise level, which is similar to the standard CS result. In the special noise free case, the recovery is exact provided that the signal is sufficiently sparse with respect to the perturbation level. The formulated structured sensing matrix perturbation is applicable to the direction of arrival estimation problem, so has practical relevance. Algorithms are proposed to implement the l1 minimization problem and numerical simulations are carried out to verify the results obtained.

Connected through hyperedge HE-1 (induced by source paper "fast single image dehazing via multilevel wavelet transform based optimization"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"
    - "dehazenet an end to end system for single image haze removal"

Connected through hyperedge HE-2 (induced by source paper "primary user localization and online radio cartography via structured tensor decomposition"):
  with members:
    - "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization"
    - "sparsity cognizant total least squares for perturbed compressive sampling"

Connected through hyperedge HE-3 (induced by source paper "sparse methods for direction of arrival estimation"):
  with members:
    - "a compact formulation for the ell_ 2 1 mixed norm minimization problem"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"

Connected through hyperedge HE-4 (induced by source paper "signal recovery in perturbed fourier compressed sensing"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration via linear least squares"
    - "self calibration and biconvex compressive sensing"
    - "compressive phase retrieval via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "compressed sensing off the grid"
    - "joint sparsity with different measurement matrices"

Connected through hyperedge HE-5 (induced by source paper "frequency selective vandermonde decomposition of toeplitz matrices with applications"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "spectral super resolution with prior knowledge"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-6 (induced by source paper "line spectral estimation based on compressed sensing with deterministic sub nyquist sampling"):
  with members:
    - "frequency estimation of multiple sinusoids with three sub nyquist channels"
    - "a class of deterministic sensing matrices and their application in harmonic detection"
    - "robust spectral compressed sensing via structured matrix completion"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "variational relevance vector machines"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-7 (induced by source paper "low complexity multiclass encryption by compressed sensing"):
  with members:
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "xampling signal acquisition and processing in union of subspaces"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "message passing algorithms for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "exact joint sparse frequency recovery via optimization methods"):
  with members:
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "continuous compressed sensing with a single or multiple measurement vectors"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "near minimax line spectral estimation"

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)