human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information"):
  with members:
    - "signal recovery under cumulative coherence"
    - "recovery of signals under the condition on ric and roc via prior support information"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-2 (induced by source paper "on multi layer basis pursuit efficient algorithms and convolutional neural networks"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep component analysis via alternating direction neural networks"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "trainlets dictionary learning in high dimensions"
    - "densely connected convolutional networks"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "signal recovery under cumulative coherence"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressed sensing matrices from fourier matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "new bounds for restricted isometry constants"

Question: Please tell me which class the center node 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)