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 "resolution limit for line spectral estimation theory and algorithm"):
  Abstract: Line spectral estimation is a classical signal processing problem aimed to estimate the spectral lines of a signal from its noisy (deterministic or random) measurements. Despite a large body of research on this subject, the theoretical understanding of the spectral line estimation is still elusive. In this paper, we quantitatively characterize the two resolution limits in the line spectral estimation problem: one is the minimum separation distance between the spectral lines that is required for an exact recovery of the number of spectral lines, and the other is the minimum separation distance between the spectral lines that is required for a stable recovery of the supports of the spectral lines. The quantitative characterization implies a phase transition phenomenon in each of the two recovery problems, and also the subtle difference between the two. Moreover, they give a sharp characterization to the resolution limit for the deconvolution problem as a consequence. Finally, we proposed a recursive MUSIC-type algorithm for the number recovery and an augmented MUSIC-algorithm for the support recovery, and analyze their performance both theoretically and numerically. The numerical results also confirm our results on the resolution limit and the phase transition phenomenon.

Includes members:
  Member "approximate support recovery of atomic line spectral estimation a tale of resolution and precision":
    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 limit of the esprit algorithm":
    also connected through other hyperedges:
      (none)
  Member "stable super resolution limit and smallest singular value of restricted fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "super resolution limit of the esprit algorithm")
  Member "super resolution of complex exponentials from modulations with unknown waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "the recoverability limit for superresolution via sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution")
  Member "super resolution extremal functions and the condition number of vandermonde matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution")
  Member "near minimax line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "atomic norm denoising with applications to line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")

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)