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: "mismatch and resolution in compressive imaging"
  Abstract: Highly coherent sensing matrices arise in discretization of continuum problems such as radar and medical#R##N#imaging when the grid spacing is below the Rayleigh threshold as well as in using highly coherent, redundant#R##N#dictionaries as sparsifying operators.#R##N#Algorithms (BOMP, BLOOMP) based on techniques of band exclusion and local optimization are proposed#R##N#to enhance Orthogonal Matching Pursuit (OMP) and deal with such coherent sensing matrices.#R##N#BOMP and BLOOMP have provably performance guarantee of reconstructing sparse, widely separated#R##N#objects independent of the redundancy and have a sparsity constraint and computational cost similar to#R##N#OMP's.#R##N#Numerical study demonstrates the effectiveness of BLOOMP for compressed sensing with highly coherent,#R##N#redundant sensing matrices.

Connected through hyperedge HE-1 (induced by source paper "resolution limit for line spectral estimation theory and algorithm"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "super resolution limit of the esprit algorithm"
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "the recoverability limit for superresolution via sparsity"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-2 (induced by source paper "sensor calibration for off the grid spectral estimation"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "blind gain and phase calibration via sparse spectral methods"
    - "from blind deconvolution to blind super resolution through convex programming"
    - "non convex phase retrieval from stft measurements"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "leveraging diversity and sparsity in blind deconvolution"
    - "optimal sample complexity for blind gain and phase calibration"

Connected through hyperedge HE-3 (induced by source paper "stable super resolution limit and smallest singular value of restricted fourier matrices"):
  with members:
    - "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"
    - "near minimax line spectral estimation"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-4 (induced by source paper "music for multidimensional spectral estimation stability and super resolution"):
  with members:
    - "super resolution of positive sources the discrete setup"
    - "the recoverability limit for superresolution via sparsity"
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "precise semidefinite programming formulation of atomic norm minimization for recovering d dimensional d geq 2 off the grid frequencies"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

Connected through hyperedge HE-5 (induced by source paper "music for single snapshot spectral estimation stability and super resolution"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "super resolution via superset selection and pruning"
    - "support detection in super resolution"
    - "super resolution by compressive sensing algorithms"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "the music algorithm for sparse objects a compressed sensing analysis"

Connected through hyperedge HE-6 (induced by source paper "super resolution by compressive sensing algorithms"):
  with members:
    - "coherence pattern guided compressive sensing with unresolved grids"

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)