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 line spectrum estimation with block priors"
  Abstract: We address the problem of super-resolution line spectrum estimation of an undersampled signal with block prior information. The component frequencies of the signal are assumed to take arbitrary continuous values in known frequency blocks. We formulate a general semidefinite program to recover these continuous-valued frequencies using theories of positive trigonometric polynomials. The proposed semidefinite program achieves super-resolution frequency recovery by taking advantage of known structures of frequency blocks. Numerical experiments show great performance enhancements using our method.

Connected through hyperedge HE-1 (induced by source paper "distributed compressed sensing off the grid"):
  with members:
    - "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"
    - "off the grid spectral compressed sensing with prior information"
    - "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 "block iterative reweighted algorithms for super resolution of spectrally sparse signals"):
  with members:
    - "spectral super resolution with prior knowledge"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "super resolution compressed sensing a generalized iterative reweighted l2 approach"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

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)