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: "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
  Abstract: The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing. However, solving the nuclear norm-based relaxed convex problem usually leads to a suboptimal solution of the original rank minimization problem. In this paper, we propose to use a family of nonconvex surrogates of    $L_{0}$   -norm on the singular values of a matrix to approximate the rank function. This leads to a nonconvex nonsmooth minimization problem. Then, we propose to solve the problem by an iteratively reweighted nuclear norm (IRNN) algorithm. IRNN iteratively solves a weighted singular value thresholding problem, which has a closed form solution due to the special properties of the nonconvex surrogate functions. We also extend IRNN to solve the nonconvex problem with two or more blocks of variables. In theory, we prove that the IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthesized data and real images demonstrate that IRNN enhances the low rank matrix recovery compared with the state-of-the-art convex algorithms.

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"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "generalized singular value thresholding"

Connected through hyperedge HE-2 (induced by source paper "an optimal condition of robust low rank matrices recovery"):
  with members:
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "nearly optimal bounds for orthogonal least squares"
    - "success probability of the babai estimators for box constrained integer linear models"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"

Connected through hyperedge HE-3 (induced by source paper "low rank and total variation regularization and its application to image recovery"):
  with members:
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "binary matrix completion with nonconvex regularizers"):
  with members:
    - "link prediction via matrix completion"
    - "positive unlabeled learning with non negative risk estimator"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "matrix completion via max norm constrained optimization"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "network topology inference from spectral templates"
    - "social trust prediction via max norm constrained 1 bit matrix completion"

Connected through hyperedge HE-5 (induced by source paper "a simple prior free method for non rigid structure from motion factorization revisited"):
  with members:
    - "jumping manifolds geometry aware dense non rigid structure from motion"
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation"
    - "robust recovery of subspace structures by low rank representation"
    - "weighted algorithms for compressed sensing and matrix completion"

Connected through hyperedge HE-6 (induced by source paper "non rigid structure from motion prior free factorization method revisited"):
  with members:
    - "superpixel soup monocular dense 3d reconstruction of a complex dynamic scene"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-7 (induced by source paper "double weighted truncated nuclear norm regularization for low rank matrix completion"):
  with members:
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "nonconvex approach for sparse and low rank constrained models with dual momentum"):
  with members:
    - "generalized singular value thresholding"
    - "robust recovery of subspace structures by low rank representation"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "robust principal component analysis"

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.LG(Machine Learning)