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 "sparse spikes deconvolution on thin grids"):
  Abstract: This article analyzes the recovery performance of two popular finite dimensional approximations of the sparse spikes deconvolution problem over Radon measures. We examine in a unified framework both the L1 regularization (often referred to as Lasso or Basis-Pursuit) and the Continuous Basis-Pursuit (C-BP) methods. The Lasso is the de-facto standard for the sparse regularization of inverse problems in imaging. It performs a nearest neighbor interpolation of the spikes locations on the sampling grid. The C-BP method, introduced by Ekanadham, Tranchina and Simoncelli, uses a linear interpolation of the locations to perform a better approximation of the infinite-dimensional optimization problem, for positive measures. We show that, in the small noise regime, both methods estimate twice the number of spikes as the number of original spikes. Indeed, we show that they both detect two neighboring spikes around the locations of an original spikes. These results for deconvolution problems are based on an abstract analysis of the so-called extended support of the solutions of L1-type problems (including as special cases the Lasso and C-BP for deconvolution), which are of an independent interest. They precisely characterize the support of the solutions when the noise is small and the regularization parameter is selected accordingly. We illustrate these findings to analyze for the first time the support instability of compressed sensing recovery when the number of measurements is below the critical limit (well documented in the literature) where the support is provably stable.

Includes members:
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "support detection in super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "super resolution from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "towards a mathematical theory of super resolution":
    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 "sharp support recovery from noisy random measurements by l1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements")

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)