human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"):
  Abstract: Abstract   This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by    O  (    1    n    )   , the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size    O  (    log  ⁡  n  /    n    3      σ  )    of one of the true frequencies. Here n is half the number of temporal samples and      σ    2      is the Gaussian noise variance. The analysis is based on a primal–dual witness construction procedure. The obtained error bound matches the Cramer–Rao lower bound up to a logarithmic factor. The relationship between resolution (separation of frequencies) and precision or accuracy of the estimator is highlighted. Our analysis also reveals that the atomic norm minimization can be viewed as a convex way to solve a      l    1     -norm regularized, nonlinear and nonconvex least-squares problem to global optimality.

Includes members:
  Member "atomic norm minimization for modal analysis from random and compressed samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recovery analysis of damped spectrally sparse signals and its relation to music")
  Member "super resolution of positive sources the discrete setup":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution")
  Member "sparse spikes deconvolution on thin grids":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stable support recovery of stream of pulses with application to ultrasound imaging")
  Member "forward backward greedy algorithms for atomic norm regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "functional nonlinear sparse models")
  Member "off the grid line spectrum denoising and estimation with multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recovery analysis of damped spectrally sparse signals and its relation to music")
  Member "exact joint sparse frequency recovery via optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  Member "near minimax line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "support detection in super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the geometry of off the grid compressed sensing")

Question: Please tell me which class the source paper 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)