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: "fast randomized singular value thresholding for nuclear norm minimization"
  Abstract: Rank minimization can be boiled down to tractable surrogate problems, such as Nuclear Norm Minimization (NNM) and Weighted NNM (WNNM). The problems related to NNM (or WNNM) can be solved iteratively by applying a closed-form proximal operator, called Singular Value Thresholding (SVT) (or Weighted SVT), but they suffer from high computational cost of computing Singular Value Decomposition (SVD) at each iteration. We propose a fast and accurate approximation method for SVT, that we call fast randomized SVT (FRSVT), where we avoid direct computation of SVD. The key idea is to extract an approximate basis for the range of a matrix from its compressed matrix. Given the basis, we compute the partial singular values of the original matrix from a small factored matrix. In addition, by adopting a range propagation technique, our method further speeds up the extraction of approximate basis at each iteration. Our theoretical analysis shows the relationship between the approximation bound of SVD and its effect to NNM via SVT. Along with the analysis, our empirical results quantitatively and qualitatively show that our approximation rarely harms the convergence of the host algorithms. We assess the efficiency and accuracy of our method on various vision problems, e.g., subspace clustering, weather artifact removal, and simultaneous multi-image alignment and rectification.

Connected through hyperedge HE-1 (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"
    - "robust principal component analysis on graphs"
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-2 (induced by source paper "large scale low rank matrix learning with nonconvex regularizers"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast low rank matrix learning with nonconvex regularization"
    - "orthogonal rank one matrix pursuit for low rank matrix completion"
    - "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"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-3 (induced by source paper "fast singular value shrinkage with chebyshev polynomial approximation based on signal sparsity"):
  with members:
    - "an algorithm for improving non local means operators via low rank approximation"
    - "low rank modeling and its applications in image analysis"
    - "convexity in source separation models geometry and algorithms"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "a fast implementation of singular value thresholding algorithm using recycling rank revealing randomized singular value decomposition"):
  with members:
    - "simple bounds for recovering low complexity models"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "compressive pca for low rank matrices on graphs"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "network topology identification from spectral templates"
    - "compressive spectral clustering"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"
    - "random sampling of bandlimited signals on graphs"

Connected through hyperedge HE-6 (induced by source paper "fast robust pca on graphs"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "stationary signal processing on graphs"
    - "robust principal component analysis on graphs"
    - "shape interaction matrix revisited and robustified efficient subspace clustering with corrupted and incomplete data"
    - "gspbox a toolbox for signal processing on graphs"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"
    - "matrix completion on graphs"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-7 (induced by source paper "compressive pca on graphs"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "stationary signal processing on graphs"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"
    - "random sampling of bandlimited signals on graphs"
    - "gspbox a toolbox for signal processing on graphs"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-8 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

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)