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 recovery with coherent tight frames via analysis dantzig selector and analysis lasso"):
  Abstract: This article considers recovery of signals that are sparse or approximately sparse in terms of a (possibly) highly overcomplete and coherent tight frame from undersampled data corrupted with additive noise. We show that the properly constrained $l_1$-analysis, called analysis Dantzig selector, stably recovers a signal which is nearly sparse in terms of a tight frame provided that the measurement matrix satisfies a restricted isometry property adapted to the tight frame. As a special case, we consider the Gaussian noise. Further, under a sparsity scenario, with high probability, the recovery error from noisy data is within a log-like factor of the minimax risk over the class of vectors which are at most $s$ sparse in terms of the tight frame. Similar results for the analysis LASSO are showed. #R##N#The above two algorithms provide guarantees only for noise that is bounded or bounded with high probability (for example, Gaussian noise). However, when the underlying measurements are corrupted by sparse noise, these algorithms perform suboptimally. We demonstrate robust methods for reconstructing signals that are nearly sparse in terms of a tight frame in the presence of bounded noise combined with sparse noise. The analysis in this paper is based on the restricted isometry property adapted to a tight frame, which is a natural extension to the standard restricted isometry property.

Includes members:
  Member "compressed sensing with general frames via optimal dual based ell _ 1 analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde")
  Member "recovery of sparsely corrupted signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "metrics for evaluating the efficiency of compressing sensing techniques")
  Member "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable meta learning of linear representations")
  Member "new and improved johnson lindenstrauss embeddings via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the mode of a kernel density estimate")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)