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: "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
  Abstract: In traditional compressed sensing theory, the dic- tionary matrix is given ap riori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper, we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of off-grid targets, encountered in both radar systems and array processing. We propose to use joint sparse signal recovery to solve the com- pressed sensing problem with structured dictionary mismatches and also give an analytical performance bound on this joint sparse recovery. We show that, under mild conditions, the reconstruction error of the original sparse signal is bounded by both the sparsity and the noise level in the measurement model. Moreover, we implement fast first-order algorithms to speed up the computing process. Numerical examples demonstrate the good performance of the proposed algorithm and also show that the joint-sparse recovery method yields a better reconstruction result than existing methods. By implementing the joint sparse recovery method, the accuracy and efficiency of DOA estimation are improved in both passive and active sensing cases.

Connected through hyperedge HE-1 (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-2 (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"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"
    - "joint sparsity with different measurement matrices"

Connected through hyperedge HE-3 (induced by source paper "super resolution delay doppler estimation for ofdm passive radar"):
  with members:
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "exact joint sparse frequency recovery via optimization methods"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"

Connected through hyperedge HE-4 (induced by source paper "a high resolution doa estimation method with a family of nonconvex penalties"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"
    - "compressed sensing off the grid"

Connected through hyperedge HE-5 (induced by source paper "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"):
  with members:
    - "on the restricted isometry of the columnwise khatri rao product"
    - "estimation of sparse mimo channels with common support"
    - "subspace methods for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "rank awareness in joint sparse recovery"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"

Connected through hyperedge HE-6 (induced by source paper "fast reconstruction algorithm for perturbed compressive sensing based on total least squares and proximal splitting"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "bilinear generalized approximate message passing part i derivation"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "on the performance bound of sparse estimation with sensing matrix perturbation"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-7 (induced by source paper "gridless quadrature compressive sampling with interpolated array technique"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "quadrature compressive sampling for radar signals"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-8 (induced by source paper "enhancing sparsity and resolution via reweighted atomic norm minimization"):
  with members:
    - "distributed compressed sensing off the grid"
    - "robust spectral compressed sensing via structured matrix completion"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "exact joint sparse frequency recovery via optimization methods"
    - "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"

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)