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: "orthonormal expansion ell_ 1 minimization algorithms for compressed sensing"
  Abstract: Compressed sensing aims at reconstructing sparse signals from significantly reduced number of samples, and a popular reconstruction approach is l1-norm minimization. In this correspondence, a method called orthonormal expansion is presented to reformulate the basis pursuit problem for noiseless compressed sensing. Two algorithms are proposed based on convex optimization: one exactly solves the problem and the other is a relaxed version of the first one. The latter can be considered as a modified iterative soft thresholding algorithm and is easy to implement. Numerical simulation shows that, in dealing with noise-free measurements of sparse signals, the relaxed version is accurate, fast and competitive to the recent state-of-the-art algorithms. Its practical application is demonstrated in a more general case where signals of interest are approximately sparse and measurements are contaminated with noise.

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 "on gridless sparse methods for line spectral estimation from complete and incomplete data"):
  with members:
    - "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"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-3 (induced by source paper "robustly stable signal recovery in compressed sensing with structured matrix perturbation"):
  with members:
    - "on phase transition of compressed sensing in the complex domain"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"
    - "blind compressed sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "on phase transition of compressed sensing in the complex domain"):
  with members:
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "compressed sensing how sharp is the restricted isometry property"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "block length dependent thresholds in block sparse compressed sensing"

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)