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: "average case analysis of multichannel sparse recovery using convex relaxation"
  Abstract: In this paper, we consider recovery of jointly sparse multichannel signals from incomplete measurements. Several approaches have been developed to recover the unknown sparse vectors from the given observations, including thresholding, simultaneous orthogonal matching pursuit (SOMP), and convex relaxation based on a mixed matrix norm. Typically, worst-case analysis is carried out in order to analyze conditions under which the algorithms are able to recover any jointly sparse set of vectors. However, such an approach is not able to provide insights into why joint sparse recovery is superior to applying standard sparse reconstruction methods to each channel individually. Previous work considered an average case analysis of thresholding and SOMP by imposing a probability model on the measured signals. In this paper, our main focus is on analysis of convex relaxation techniques. In particular, we focus on the mixed l_2,1 approach to multichannel recovery. We show that under a very mild condition on the sparsity and on the dictionary characteristics, measured for example by the coherence, the probability of recovery failure decays exponentially in the number of channels. This demonstrates that most of the time, multichannel sparse recovery is indeed superior to single channel methods. Our probability bounds are valid and meaningful even for a small number of signals. Using the tools we develop to analyze the convex relaxation method, we also tighten the previous bounds for thresholding and SOMP.

Connected through hyperedge HE-1 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior"):
  with members:
    - "inference under information constraints i lower bounds from chi square contraction"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "performance analysis for sparse support recovery"
    - "necessary and sufficient conditions for sparsity pattern recovery"

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 "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "millimeter wave beam selection using out of band spatial information"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-4 (induced by source paper "multi snapshot newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"

Connected through hyperedge HE-5 (induced by source paper "analysis of hard thresholding for distributed compressed sensing with one bit measurements"):
  with members:
    - "recovering structured data from superimposed non linear measurements"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "the restricted isometry property for random block diagonal matrices"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-6 (induced by source paper "enhanced signal recovery via sparsity inducing image priors"):
  with members:
    - "sparsity based color image super resolution via exploiting cross channel constraints"
    - "deep convolutional neural network for inverse problems in imaging"
    - "accelerating the super resolution convolutional neural network"
    - "detecting dominant vanishing points in natural scenes with application to composition sensitive image retrieval"
    - "screen content image segmentation using sparse smooth decomposition"
    - "accurate image super resolution using very deep convolutional networks"
    - "self tuned deep super resolution"
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"

Connected through hyperedge HE-7 (induced by source paper "on the sample complexity of multichannel frequency estimation via convex optimization"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "the restricted isometry property of subsampled fourier matrices"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"

Connected through hyperedge HE-8 (induced by source paper "improved support recovery guarantees for the group lasso with applications to structural health monitoring"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on block coherence of frames"
    - "the degrees of freedom of the group lasso for a general design"
    - "subspace methods for joint sparse recovery"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "recovery of block sparse representations from noisy observations via orthogonal matching pursuit"
    - "structured compressed sensing from theory to applications"

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)