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: "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
  Abstract: Signal recovery from a given set of linear measurements using a sparsity prior has been a major subject of research in recent years. In this model, the signal is assumed to have a sparse representation under a given dictionary. Most of the work dealing with this subject has focused on the reconstruction of the signal's representation as the means for recovering the signal itself. This approach forced the dictionary to be of low coherence and with no linear dependencies between its columns. Recently, a series of contributions that focus on signal recovery using the analysis model find that linear dependencies in the analysis dictionary are in fact permitted and beneficial. In this paper we show theoretically that the same holds also for signal recovery in the synthesis case for the l0- synthesis minimization problem. In addition, we demonstrate empirically the relevance of our conclusions for recovering the signal using an l1-relaxation.

Connected through hyperedge HE-1 (induced by source paper "tolerant compressed sensing with partially coherent sensing matrices"):
  with members:
    - "super resolution from noisy data"
    - "sub nyquist radar via doppler focusing"
    - "analysis of block omp using block rip"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-2 (induced by source paper "spark level sparsity and the ell_1 tail minimization"):
  with members:
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "sparse signal reconstruction via iterative support detection"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "sampling in the analysis transform domain"):
  with members:
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "analysis operator learning and its application to image reconstruction"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "stable and robust sampling strategies for compressive imaging"
    - "optimization with sparsity inducing penalties"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-4 (induced by source paper "sparsity based methods for overparameterized variational problems"):
  with members:
    - "using correlated subset structure for compressive sensing recovery"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "towards a mathematical theory of super resolution"
    - "stable and robust sampling strategies for compressive imaging"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "on the effective measure of dimension in the analysis cosparse model"):
  with members:
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "stable image reconstruction using total variation minimization"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "matrix completion with noise"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "a null space property approach to compressed sensing with frames"):
  with members:
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "performance analysis of ell_1 synthesis with coherent frames"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"

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)