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: "theoretical and empirical results for recovery from multiple measurements"
  Abstract: The joint-sparse recovery problem aims to recover, from sets of compressed measurements, unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an extension of the single-measurement-vector (SMV) problem widely studied in compressed sensing. We study the recovery properties of two algorithms for problems with noiseless data and exact-sparse representation. First, we show that recovery using sum-of-norm minimization cannot exceed the uniform-recovery rate of sequential SMV using l 1 minimization, and that there are problems that can be solved with one approach, but not the other. Second, we study the performance of the ReMBo algorithm (M. Mishali and Y. Eldar, ?Reduce and boost: Recovering arbitrary sets of jointly sparse vectors,? IEEE Trans. Signal Process., vol. 56, no. 10, 4692-4702, Oct. 2008) in combination with l 1 minimization, and show how recovery improves as more measurements are taken. From this analysis, it follows that having more measurements than the number of linearly independent nonzero rows does not improve the potential theoretical recovery rate.

Connected through hyperedge HE-1 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery"):
  with members:
    - "gelfand numbers related to structured sparsity and besov space embeddings with small mixed smoothness"
    - "uniform recovery from subgaussian multi sensor measurements"
    - "uniform recovery of fusion frame structured sparse signals"
    - "the restricted isometry property for random block diagonal matrices"
    - "model based compressive sensing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "performance analysis of approximate message passing for distributed compressed sensing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "sparse multinomial logistic regression via approximate message passing"
    - "signal estimation with additive error metrics in compressed sensing"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "performance regions in compressed sensing from noisy measurements"

Connected through hyperedge HE-4 (induced by source paper "compressed anomaly detection with multiple mixed observations"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "compressed hypothesis testing to mix or not to mix"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "on the limits of sequential testing in high dimensions"
    - "statistical compressed sensing of gaussian mixture models"
    - "iterative hard thresholding for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-5 (induced by source paper "performance limits for noisy multimeasurement vector problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "phase transitions in sparse pca"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "performance regions in compressed sensing from noisy measurements"
    - "subspace methods for joint sparse recovery"

Connected through hyperedge HE-6 (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"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "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"
    - "rank awareness in joint sparse recovery"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"

Connected through hyperedge HE-7 (induced by source paper "analysis of equivalence relation in joint sparse recovery"):
  with members:
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"):
  with members:
    - "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "perturbation analysis of orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"

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)