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: "off the grid spectral compressed sensing with prior information"
  Abstract: Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In this paper, we extend off-the-grid CS to applications where some prior information about spectrally sparse signal is known. We specifically consider cases where a few contributing frequencies or poles, but not their amplitudes or phases, are known a priori. Our results show that equipping off-the-grid CS with the known-poles algorithm can increase the probability of recovering all the frequency components.

Connected through hyperedge HE-1 (induced by source paper "distributed compressed sensing off the grid"):
  with members:
    - "super resolution line spectrum estimation with block priors"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "spectral compressive sensing with polar interpolation"
    - "spectral compressed sensing via structured matrix completion"
    - "joint sparsity recovery for spectral compressed sensing"
    - "spectral compressive sensing with model selection"

Connected through hyperedge HE-2 (induced by source paper "enhancing sparsity and resolution via reweighted atomic norm minimization"):
  with members:
    - "distributed compressed sensing off the grid"
    - "robust spectral compressed sensing via structured matrix completion"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "exact joint sparse frequency recovery via optimization methods"
    - "continuous compressed sensing with a single or multiple measurement vectors"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing with prior information optimal strategies geometry and bounds"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "sharp mse bounds for proximal denoising"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "structured compressed sensing from theory to applications"
    - "improved bounds on restricted isometry constants for gaussian matrices"

Connected through hyperedge HE-4 (induced by source paper "super resolution line spectrum estimation with block priors"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "structured compressed sensing from theory to applications"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "model based compressive sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "modified cs modifying compressive sensing for problems with partially known support"

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)