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: "robust sparse analysis regularization"
  Abstract: This paper investigates the theoretical guarantees of L1-analysis regularization when solving linear inverse problems. Most of previous works in the literature have mainly focused on the sparse synthesis prior where the sparsity is measured as the L1 norm of the coefficients that synthesize the signal from a given dictionary. In contrast, the more general analysis regularization minimizes the L1 norm of the correlations between the signal and the atoms in the dictionary, where these correlations define the analysis support. The corresponding variational problem encompasses several well-known regularizations such as the discrete total variation and the Fused Lasso. Our main contributions consist in deriving sufficient conditions that guarantee exact or partial analysis support recovery of the true signal in presence of noise. More precisely, we give a sufficient condition to ensure that a signal is the unique solution of the L1-analysis regularization in the noiseless case. The same condition also guarantees exact analysis support recovery and L2-robustness of the L1-analysis minimizer vis-a-vis an enough small noise in the measurements. This condition turns to be sharp for the robustness of the analysis support. To show partial support recovery and L2-robustness to an arbitrary bounded noise, we introduce a stronger sufficient condition. When specialized to the L1-synthesis regularization, our results recover some corresponding recovery and robustness guarantees previously known in the literature. From this perspective, our work is a generalization of these results. We finally illustrate these theoretical findings on several examples to study the robustness of the 1-D total variation and Fused Lasso regularizations.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery"):
  with members:
    - "total variation minimization in compressed sensing"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "the generalized lasso with non linear observations"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "sharp mse bounds for proximal denoising"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-2 (induced by source paper "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"):
  with members:
    - "learning co sparse analysis operators with separable structures"
    - "precise phase transition of total variation minimization"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "on the effective measure of dimension in the analysis cosparse model"
    - "projection onto the cosparse set is np hard"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-3 (induced by source paper "sparse depth sensing for resource constrained robots"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "compressed sensing for scalable robotic tactile skins"
    - "deeper depth prediction with fully convolutional residual networks"
    - "adaptive foveated single pixel imaging with dynamic super sampling"
    - "orb slam a versatile and accurate monocular slam system"
    - "high performance and tunable stereo reconstruction"
    - "depth reconstruction from sparse samples representation algorithm and sampling"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-4 (induced by source paper "sparse support recovery with non smooth loss functions"):
  with members:
    - "sparse spikes deconvolution on thin grids"
    - "on the optimality of a l1 l1 solver for sparse signal recovery from sparsely corrupted compressive measurements"
    - "recovery of sparsely corrupted signals"
    - "consistency of trace norm minimization"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-5 (induced by source paper "multi dimensional signal approximation with sparse structured priors using split bregman iterations"):
  with members:
    - "multi dimensional sparse structured signal approximation using split bregman iterations"

Connected through hyperedge HE-6 (induced by source paper "a nonconvex approach for structured sparse learning"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "stable image reconstruction using total variation minimization"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"

Connected through hyperedge HE-7 (induced by source paper "stable cosparse recovery via ell_q analysis optimization"):
  with members:
    - "stable image reconstruction using total variation minimization"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"

Connected through hyperedge HE-8 (induced by source paper "generalized approximate message passing for cosparse analysis compressive sensing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "sparsity averaging for compressive imaging"

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)