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: "spectral compressed sensing via structured matrix completion"
  Abstract: The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples. Specifically, the object of interest with ambient dimension $n$ is assumed to be a mixture of $r$ complex multi-dimensional sinusoids, while the underlying frequencies can assume any value in the unit disk. Conventional compressed sensing paradigms suffer from the {\em basis mismatch} issue when imposing a discrete dictionary on the Fourier representation. To address this problem, we develop a novel nonparametric algorithm, called enhanced matrix completion (EMaC), based on structured matrix completion. The algorithm starts by arranging the data into a low-rank enhanced form with multi-fold Hankel structure, then attempts recovery via nuclear norm minimization. Under mild incoherence conditions, EMaC allows perfect recovery as soon as the number of samples exceeds the order of $\mathcal{O}(r\log^{2} n)$. We also show that, in many instances, accurate completion of a low-rank multi-fold Hankel matrix is possible when the number of observed entries is proportional to the information theoretical limits (except for a logarithmic gap). The robustness of EMaC against bounded noise and its applicability to super resolution are further demonstrated by numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "two dimensional super resolution via convex relaxation"):
  with members:
    - "off the grid two dimensional line spectral estimation with prior information"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "spectral super resolution with prior knowledge"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-2 (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"
    - "joint sparsity recovery for spectral compressed sensing"
    - "spectral compressive sensing with model selection"

Connected through hyperedge HE-3 (induced by source paper "incoherence optimal matrix completion"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "hidden cliques and the certification of the restricted isometry property"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "note on sampling without replacing from a finite collection of matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"

Connected through hyperedge HE-4 (induced by source paper "efficient structured matrix rank minimization"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-5 (induced by source paper "robust spectral compressed sensing via structured matrix completion"):
  with members:
    - "incoherence optimal matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "near minimax line spectral estimation"
    - "compressive demodulation of mutually interfering signals"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "compressed sensing and matrix completion with constant proportion of corruptions"

Connected through hyperedge HE-6 (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"
    - "joint sparsity recovery for spectral compressed sensing"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "joint sparsity recovery for spectral compressed sensing"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "towards a mathematical theory of super resolution"
    - "subspace methods for joint sparse recovery"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "rank awareness in joint sparse recovery"

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)