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: "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
  Abstract: We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non-negative amplitude parameters to arbitrary complex ones, and (ii) we allow for mismatch between the manifold described by the parameters and its polar approximation. To quantify the improvements afforded by the proposed extensions, we evaluate six algorithms for estimation of parameters in sparse translation-invariant signals, exemplified with the time delay estimation problem. The evaluation is based on three performance metrics: estimator precision, sampling rate and computational complexity. We use compressive sensing with all the algorithms to lower the necessary sampling rate and show that it is still possible to attain good estimation precision and keep the computational complexity low. Our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency-estimation problem followed by a super-resolution algorithm. The algorithms studied here provide various tradeoffs between computational complexity, estimation precision, and necessary sampling rate. The work shows that compressive sensing for the class of sparse translation-invariant signals allows for a decrease in sampling rate and that the use of polar interpolation increases the estimation precision.

Connected through hyperedge HE-1 (induced by source paper "compressive parameter estimation via k median clustering"):
  with members:
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "robust spectral compressed sensing via structured matrix completion"
    - "matched filtering from limited frequency samples"
    - "spectral compressive sensing with polar interpolation"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

Connected through hyperedge HE-2 (induced by source paper "a general and yet efficient scheme for sub nyquist radar processing"):
  with members:
    - "a general sequential delay doppler estimation scheme for sub nyquist pulse doppler radar"
    - "gridless quadrature compressive sampling with interpolated array technique"
    - "quadrature compressive sampling for radar signals"
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-3 (induced by source paper "a general sequential delay doppler estimation scheme for sub nyquist pulse doppler radar"):
  with members:
    - "gridless quadrature compressive sampling with interpolated array technique"
    - "quadrature compressive sampling for radar signals"
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-4 (induced by source paper "variational bayesian inference of line spectra"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-5 (induced by source paper "off the grid line spectrum denoising and estimation with multiple measurement vectors"):
  with members:
    - "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"
    - "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-6 (induced by source paper "compressed subspace matching on the continuum"):
  with members:
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "compressive parameter estimation in awgn"
    - "matched filtering from limited frequency samples"
    - "near minimax line spectral estimation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "blind deconvolution using convex programming"
    - "stable manifold embeddings with structured random matrices"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-7 (induced by source paper "compressive imaging and characterization of sparse light deflection maps"):
  with members:
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "spread spectrum magnetic resonance imaging"
    - "theory and applications of compressed sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressive schlieren deflectometry"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-8 (induced by source paper "spectral compressive sensing with model selection"):
  with members:
    - "spectral compressive sensing with polar interpolation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "structured compressed sensing from theory to applications"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

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)