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: "generalized nonconvex nonsmooth low rank minimization"
  Abstract: As surrogate functions of 0-norm, many nonconvex penalty functions have been proposed to enhance the sparse vector recovery. It is easy to extend these nonconvex penalty functions on singular values of a matrix to enhance low-rank matrix recovery. However, different from convex optimization, solving the nonconvex low-rank minimization problem is much more challenging than the nonconvex sparse minimization problem. We observe that all the existing nonconvex penalty functions are concave and monotonically increasing on [0, ∞). Thus their gradients are decreasing functions. Based on this property, we propose an Iteratively Reweighted Nuclear Norm (IRNN) algorithm to solve the nonconvex nonsmooth low-rank minimization problem. IRNN iteratively solves a Weighted Singular Value Thresholding (WSVT) problem. By setting the weight vector as the gradient of the concave penalty function, the WSVT problem has a closed form solution. In theory, we prove that IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthetic data and real images demonstrate that IRNN enhances the low-rank matrix recovery compared with 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"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "generalized singular value thresholding"

Connected through hyperedge HE-2 (induced by source paper "online high rank matrix completion"):
  with members:
    - "guaranteed matrix completion via non convex factorization"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "online matrix completion and online robust pca"
    - "understanding alternating minimization for matrix completion"
    - "sparse subspace clustering algorithm theory and applications"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "polynomial matrix completion for missing data imputation and transductive learning"):
  with members:
    - "why are big data matrices approximately low rank"
    - "guaranteed matrix completion via non convex factorization"
    - "adam a method for stochastic optimization"
    - "sparse subspace clustering algorithm theory and applications"
    - "high rank matrix completion and subspace clustering with missing data"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "an iteratively re weighted method for problems with sparsity inducing norms"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive survey for low rank regularization"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "side information for face completion a robust pca approach"
    - "fast and accurate tensor completion with tensor trains a system identification approach"
    - "discriminative block diagonal representation learning for image recognition"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "large scale low rank matrix learning with nonconvex regularizers"

Connected through hyperedge HE-6 (induced by source paper "low rank tensor completion for multiway visual data"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "simultaneous tensor completion and denoising by noise inequality constrained convex optimization"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "low rank tensor completion by truncated nuclear norm regularization"
    - "low tubal rank tensor completion using alternating minimization"
    - "scalable algorithms for tractable schatten quasi norm minimization"

Connected through hyperedge HE-7 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "guaranteed matrix completion via non convex factorization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "robust principal component analysis on graphs"
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-8 (induced by source paper "from rank estimation to rank approximation rank residual constraint for image denoising"):
  with members:
    - "the perception distortion tradeoff"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "adaptive image denoising by mixture adaptation"
    - "image denoising via group sparsity residual constraint"
    - "partial sum minimization of singular values in robust pca algorithm and applications"

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.CV(Computer Vision and Pattern Recognition)