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 sparsity recovery for spectral compressed sensing"
  Abstract: Compressed Sensing (CS) is an effective approach to reduce the required number of samples for reconstructing a sparse signal in an a priori basis, but may suffer severely from the issue of basis mismatch. In this paper we study the problem of simultaneously recovering multiple spectrally-sparse signals that are supported on the same frequencies lying arbitrarily on the unit circle. We propose an atomic norm minimization problem, which can be regarded as a continuous counterpart of the discrete CS formulation and be solved efficiently via semidefinite programming. Through numerical experiments, we show that the number of samples per signal may be further reduced by harnessing the joint sparsity pattern of multiple signals.

Connected through hyperedge HE-1 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array"):
  with members:
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "fundamental limits of training based multiuser mimo systems"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "channel estimation in massive mimo systems"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"

Connected through hyperedge HE-2 (induced by source paper "wideband massive mimo channel estimation via sequential atomic norm minimization"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "parametric channel estimation for massive mimo"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-3 (induced by source paper "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"):
  with members:
    - "self calibration and biconvex compressive sensing"
    - "super resolution on the sphere using convex optimization"
    - "stable separation and super resolution of mixture models"
    - "a unified framework for identifiability analysis in bilinear inverse problems with applications to subspace and sparsity models"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "support detection in super resolution"

Connected through hyperedge HE-4 (induced by source paper "distributed compressed sensing off the grid"):
  with members:
    - "super resolution line spectrum estimation with block priors"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "off the grid spectral compressed sensing with prior information"
    - "spectral compressive sensing with polar interpolation"
    - "spectral compressed sensing via structured matrix completion"
    - "spectral compressive sensing with model selection"

Connected through hyperedge HE-5 (induced by source paper "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction"):
  with members:
    - "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise"
    - "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "off the grid line spectrum denoising and estimation with multiple measurement vectors"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "spectral compressed sensing via structured matrix completion"
    - "precise semidefinite programming formulation of atomic norm minimization for recovering d dimensional d geq 2 off the grid frequencies"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

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)