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 via superset selection and pruning"
  Abstract: We present a pursuit-like algorithm that we call the "superset method" for recovery of sparse vectors from consecutive Fourier measurements in the super-resolution regime. The algorithm has a subspace identification step that hinges on the translation invariance of the Fourier transform, followed by a removal step to estimate the solution's support. The superset method is always successful in the noiseless regime (unlike L1-minimization) and generalizes to higher dimensions (unlike the matrix pencil method). Relative robustness to noise is demonstrated numerically.

Connected through hyperedge HE-1 (induced by source paper "compressive spectral estimation with single snapshot esprit stability and resolution"):
  with members:
    - "music for single snapshot spectral estimation stability and super resolution"
    - "towards a mathematical theory of super resolution"
    - "the music algorithm for sparse objects a compressed sensing analysis"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-2 (induced by source paper "stable extrapolation of analytic functions"):
  with members:
    - "the recoverability limit for superresolution via sparsity"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-3 (induced by source paper "greed is super a fast algorithm for super resolution"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "matched filtering from limited frequency samples"
    - "spectral compressive sensing with polar interpolation"
    - "near minimax line spectral estimation"
    - "a tensor based method for missing traffic data completion"
    - "support detection in super resolution"
    - "super resolution from noisy data"

Connected through hyperedge HE-4 (induced by source paper "super resolution in phase space"):
  with members:
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "xampling at the rate of innovation"
    - "towards a mathematical theory of super resolution"
    - "time delay estimation from low rate samples a union of subspaces approach"

Connected through hyperedge HE-5 (induced by source paper "super resolution radar"):
  with members:
    - "super resolution of positive sources the discrete setup"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional 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"
    - "atomic norm denoising with applications to line spectral estimation"
    - "identification of sparse linear operators"
    - "compressed sensing off the grid"

Connected through hyperedge HE-6 (induced by source paper "music for single snapshot spectral estimation stability and super resolution"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "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"
    - "mismatch and resolution in compressive imaging"
    - "the music algorithm for sparse objects a compressed sensing analysis"

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)