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: "iterative concave rank approximation for recovering low rank matrices"
  Abstract: In this paper, we propose a new algorithm for recovery of low-rank matrices from compressed linear measurements. The underlying idea of this algorithm is to closely approximate the rank function with a smooth function of singular values, and then minimize the resulting approximation subject to the linear constraints. The accuracy of the approximation is controlled via a scaling parameter δ, where a smaller δ corresponds to a more accurate fitting. The consequent optimization problem for any finite δ is nonconvex. Therefore, to decrease the risk of ending up in local minima, a series of optimizations is performed, starting with optimizing a rough approximation (a large δ) and followed by successively optimizing finer approximations of the rank with smaller δ's. To solve the optimization problem for any δ > 0, it is converted to a new program in which the cost is a function of two auxiliary positive semidefinite variables. The paper shows that this new program is concave and applies a majorize-minimize technique to solve it which, in turn, leads to a few convex optimization iterations. This optimization scheme is also equivalent to a reweighted Nuclear Norm Minimization (NNM). For any δ > 0, we derive a necessary and sufficient condition for the exact recovery which are weaker than those corresponding to NNM. On the numerical side, the proposed algorithm is compared to NNM and a reweighted NNM in solving affine rank minimization and matrix completion problems showing its considerable and consistent superiority in terms of success rate.

Connected through hyperedge HE-1 (induced by source paper "ell_0 motivated low rank sparse subspace clustering"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "multi view low rank sparse subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "enhanced low rank matrix approximation"
    - "improving compressed sensing with the diamond norm"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "generalized singular value thresholding"

Connected through hyperedge HE-2 (induced by source paper "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"):
  with members:
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "proximal iteratively reweighted algorithm with multiple splitting for nonconvex sparsity optimization"
    - "group sparse beamforming for green cloud ran"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-3 (induced by source paper "a high resolution doa estimation method with a family of nonconvex penalties"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"
    - "compressed sensing off the grid"

Connected through hyperedge HE-4 (induced by source paper "successive concave sparsity approximation for compressed sensing"):
  with members:
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "a class of nonconvex penalties preserving overall convexity in optimization based mean filtering"):
  with members:
    - "enhanced low rank matrix approximation"
    - "successive concave sparsity approximation for compressed sensing"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"

Connected through hyperedge HE-6 (induced by source paper "successive concave sparsity approximation near oracle performance in a wide range of sparsity levels"):
  with members:
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "upper bounds on the error of sparse vector and low rank matrix recovery"):
  with members:
    - "successive concave sparsity approximation for compressed sensing"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "on the error of estimating the sparsest solution of underdetermined linear systems"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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)