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 "super resolution limit of the esprit algorithm"):
  Abstract: The problem of imaging point objects can be formulated as estimation of an unknown atomic measure from its $M+1$ consecutive noisy Fourier coefficients. The standard resolution of this inverse problem is $1/M$ and super-resolution refers to the capability of resolving atoms at a higher resolution. When any two atoms are less than $1/M$ apart, this recovery problem is highly challenging and many existing algorithms either cannot deal with this situation or require restrictive assumptions on the sign of the measure. ESPRIT is an efficient method that does not depend on the sign of the measure. This paper provides an explicit error bound on the support matching distance of ESPRIT in terms of the minimum singular value of Vandermonde matrices. When the support consists of multiple well-separated clumps and noise is sufficiently small, the support error by ESPRIT scales like ${\rm SRF}^{2\lambda+2} \times {\rm Noise}$, where the Super-Resolution Factor (${\rm SRF}$) governs the difficulty of the problem and $\lambda$ is the cardinality of the largest clump. {If the support contains one clump of closely spaced atoms, the min-max error is ${\rm SRF}^{2\lambda +2} \times {\rm Noise}/M$. Our error bound matches the min-max rate up to a factor of $M$ in the small noise regime. Our results therefore establishes the near-optimality of ESPRIT,} and our theory is validated by numerical experiments.

Includes members:
  Member "stable super resolution limit and smallest singular value of restricted fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "elementary l error estimates for super resolution de noising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music")
  Member "compressive spectral estimation with single snapshot esprit stability and resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the sample complexity of multichannel frequency estimation via convex optimization")
  Member "deterministic performance analysis of subspace methods for cisoid parameter estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive sampling using annihilating filter based low rank interpolation")
  Member "super resolution of positive sources the discrete setup":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "the recoverability limit for superresolution via sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "music for single snapshot spectral estimation stability and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")

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)