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: "identification of sparse linear operators"
  Abstract: We consider the problem of identifying a linear deterministic operator from its response to a given probing signal. For a large class of linear operators, we show that stable identifiability is possible if the total support area of the operator's spreading function satisfies D<=1/2. This result holds for an arbitrary (possibly fragmented) support region of the spreading function, does not impose limitations on the total extent of the support region, and, most importantly, does not require the support region to be known prior to identification. Furthermore, we prove that stable identifiability of almost all operators is possible if D<1. This result is surprising as it says that there is no penalty for not knowing the support region of the spreading function prior to identification. Algorithms that provably recover all operators with D<=1/2, and almost all operators with D<1 are presented.

Connected through hyperedge HE-1 (induced by source paper "dynamic mode decomposition for compressive system identification"):
  with members:
    - "using dynamic mode decomposition for real time background foreground separation in video"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-2 (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"
    - "super resolution of positive sources the discrete setup"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "super resolution radar"
    - "atomic norm denoising with applications to line spectral estimation"
    - "analysis of sparse mimo radar"
    - "compressed sensing off the grid"

Connected through hyperedge HE-3 (induced by source paper "sampling and reconstruction of operators"):
  with members:
    - "cornerstones of sampling of operator theory"
    - "sampling of stochastic operators"
    - "compressive identification of linear operators"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "the restricted isometry property for time frequency structured random matrices"

Connected through hyperedge HE-4 (induced by source paper "cornerstones of sampling of operator theory"):
  with members:
    - "sampling and reconstruction of operators"
    - "estimation of overspread scattering functions"
    - "sampling of stochastic operators"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "the restricted isometry property for time frequency structured random matrices"

Connected through hyperedge HE-5 (induced by source paper "identification of linear time varying systems through waveform diversity"):
  with members:
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"

Connected through hyperedge HE-6 (induced by source paper "density criteria for the identification of linear time varying systems"):
  with members:
    - "identification of parametric underspread linear systems and super resolution radar"

Connected through hyperedge HE-7 (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"
    - "super resolution via superset selection and pruning"
    - "atomic norm denoising with applications to line spectral estimation"
    - "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)