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: "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
  Abstract: Compressed sensing with sparse frame representations is seen to have much greater range of practical applications than that with orthonormal bases. In such settings, one approach to recover the signal is known as l1-analysis. We expand in this paper the performance analysis of this approach by providing a weaker recovery condition than existing results in the literature. Our analysis is also broadly based on general frames and alter native dual frames (as analysis operators). As one application to such a general-dual-based approach and performance analysis, an optimal-dual-based technique is proposed to demonstrate the effectiveness of using alternative dual frames as l1-analysis operators. An iterative algorithm is outlined for solving the optimal-dual-based -analysis problem. The effectiveness of the proposed method and algorithm is demonstrated through several experiments.

Connected through hyperedge HE-1 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde"):
  with members:
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "analyzing the structure of multidimensional compressed sensing problems through coherence"
    - "a partial derandomization of phaselift using spherical designs"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "sampling in the analysis transform domain"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "ripless compressed sensing from anisotropic measurements"

Connected through hyperedge HE-2 (induced by source paper "local sparsity and recovery of fusion frames structured signals"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "uniform recovery of fusion frame structured sparse signals"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "on partial sparse recovery"
    - "sparse recovery from combined fusion frame measurements"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (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"
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "sparse signal reconstruction via iterative support detection"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "restricted q isometry properties adapted to frames for nonconvex l_q analysis"):
  with members:
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "compressive sensing with redundant dictionaries and structured measurements"):
  with members:
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "sampling in the analysis transform domain"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "super resolution from noisy data"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "towards a mathematical theory of super resolution"

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"
    - "robust sparse analysis regularization"

Connected through hyperedge HE-7 (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"
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "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-8 (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"
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "stable image reconstruction using total variation minimization"
    - "matrix completion with noise"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

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)