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: "super resolution of positive sources the discrete setup"
  Abstract: In single-molecule microscopy it is necessary to locate with high precision point sources from noisy observations of the spectrum of the signal at frequencies capped by $f_c$, which is just about the frequency of natural light. This paper rigorously establishes that this super-resolution problem can be solved via linear programming in a stable manner. We prove that the quality of the reconstruction crucially depends on the Rayleigh regularity of the support of the signal; that is, on the maximum number of sources that can occur within a square of side length about $1/f_c$. The theoretical performance guarantee is complemented with a converse result showing that our simple convex program convex is nearly optimal. Finally, numerical experiments illustrate our methods.

Connected through hyperedge HE-1 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"):
  with members:
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "sparse spikes deconvolution on thin grids"
    - "forward backward greedy algorithms for atomic norm regularization"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "near minimax line spectral estimation"
    - "support detection in super resolution"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-2 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "a tight converse to the spectral resolution limit via convex programming"
    - "a theory of super resolution from short time fourier transform measurements"
    - "the recoverability limit for superresolution via sparsity"
    - "robust recovery of stream of pulses using convex optimization"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"

Connected through hyperedge HE-3 (induced by source paper "super resolution limit of the esprit algorithm"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "elementary l error estimates for super resolution de noising"
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "deterministic performance analysis of subspace methods for cisoid parameter estimation"
    - "the recoverability limit for superresolution via sparsity"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "compressed sensing off the grid"

Connected through hyperedge HE-4 (induced by source paper "when does omp achieve support recovery with continuous dictionaries"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "robust recovery of positive stream of pulses"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "towards a mathematical theory of super resolution"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "learning gaussian mixtures with arbitrary separation"

Connected through hyperedge HE-5 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "elementary l error estimates for super resolution de noising"
    - "music for multidimensional spectral estimation stability and super resolution"
    - "the recoverability limit for superresolution via sparsity"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "super resolution from noisy data"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-6 (induced by source paper "superresolution method for data deconvolution by superposition of point sources"):
  with members:
    - "sparsity cognizant total least squares for perturbed compressive sampling"

Connected through hyperedge HE-7 (induced by source paper "super resolution of complex exponentials from modulations with unknown waveforms"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "self calibration and biconvex compressive sensing"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-8 (induced by source paper "super resolution mimo radar"):
  with members:
    - "a theory of super resolution from short time fourier transform measurements"
    - "refined analysis of sparse mimo radar"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "super resolution radar"
    - "atomic norm denoising with applications to line spectral estimation"
    - "identification of sparse linear operators"
    - "analysis of sparse mimo radar"
    - "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)