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: "recovery of sparse positive signals on the sphere from low resolution measurements"
  Abstract: This letter considers the problem of recovering a positive stream of Diracs on a sphere from its projection onto the space of low-degree spherical harmonics, namely, from its low-resolution version. We suggest recovering the Diracs via a tractable convex optimization problem. The resulting recovery error is proportional to the noise level and depends on the density of the Diracs. We validate the theory by numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "the geometry of off the grid compressed sensing"):
  with members:
    - "support localization and the fisher metric for off the grid sparse regularization"
    - "breaking the curse of dimensionality with convex neural networks"
    - "support detection in super resolution"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "recovering low rank matrices from few coefficients in any basis"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "a dual certificates analysis of compressive off the grid recovery"):
  with members:
    - "breaking the curse of dimensionality with convex neural networks"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "support detection in super resolution"
    - "towards a mathematical theory of super resolution"
    - "analysis and extension of arc cosine kernels for large margin classification"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "stable support recovery of stream of pulses with application to ultrasound imaging"):
  with members:
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "robust recovery of positive stream of pulses"
    - "robust recovery of stream of pulses using convex optimization"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"
    - "exact recovery of non uniform splines from the projection onto spaces of algebraic polynomials"
    - "super resolution extremal functions and the condition number of vandermonde matrices"

Connected through hyperedge HE-4 (induced by source paper "robust recovery of positive stream of pulses"):
  with members:
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "stable support recovery of stream of pulses with application to ultrasound imaging"
    - "music for multidimensional spectral estimation stability and super resolution"
    - "super resolution of positive sources the discrete setup"
    - "support recovery for sparse deconvolution of positive measures"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "the recoverability limit for superresolution via sparsity"

Connected through hyperedge HE-5 (induced by source paper "robust recovery of stream of pulses using convex optimization"):
  with members:
    - "stable support recovery of stream of pulses with application to ultrasound imaging"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "robust recovery of positive stream of pulses"
    - "spectral super resolution with prior knowledge"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"

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)