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 singular value thresholding"
  Abstract: This work studies the Generalized Singular Value Thresholding (GSVT) operator ${\text{Prox}}_{g}^{{\sigma}}(\cdot)$, \begin{equation*} #R##N#{\text{Prox}}_{g}^{{\sigma}}(B)=\arg\min\limits_{X}\sum_{i=1}^{m}g(\sigma_{i}(X)) + \frac{1}{2}||X-B||_{F}^{2}, \end{equation*} associated with a nonconvex function $g$ defined on the singular values of $X$. We prove that GSVT can be obtained by performing the proximal operator of $g$ (denoted as $\text{Prox}_g(\cdot)$) on the singular values since $\text{Prox}_g(\cdot)$ is monotone when $g$ is lower bounded. If the nonconvex $g$ satisfies some conditions (many popular nonconvex surrogate functions, e.g., $\ell_p$-norm, $0<p<1$, of $\ell_0$-norm are special cases), a general solver to find $\text{Prox}_g(b)$ is proposed for any $b\geq0$. GSVT greatly generalizes the known Singular Value Thresholding (SVT) which is a basic subroutine in many convex low rank minimization methods. We are able to solve the nonconvex low rank minimization problem by using GSVT in place of SVT.

Connected through hyperedge HE-1 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization"):
  with members:
    - "image restoration by iterative denoising and backward projections"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "learning deep cnn denoiser prior for image restoration"
    - "enhanced low rank matrix approximation"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "partial sum minimization of singular values in robust pca algorithm and applications"

Connected through hyperedge HE-2 (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 nonconvex nonsmooth low rank minimization"

Connected through hyperedge HE-3 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "tensor n tubal rank and its convex relaxation for low rank tensor recovery"
    - "exact tensor completion from sparsely corrupted observations via convex optimization"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

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"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"

Connected through hyperedge HE-5 (induced by source paper "learning vector valued functions with local rademacher complexity"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "a survey on multi task learning"
    - "orthogonal random features"
    - "a vector contraction inequality for rademacher complexities"
    - "an overview of gradient descent optimization algorithms"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "learning vector valued functions with local rademacher complexity and unlabeled data"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "data dependent generalization bounds for multi class classification"
    - "a survey on multi task learning"
    - "orthogonal random features"
    - "a vector contraction inequality for rademacher complexities"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "adam a method for stochastic optimization"
    - "multiclass learnability and the erm principle"

Connected through hyperedge HE-7 (induced by source paper "multi band weighted l_p norm minimization for image denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "partial sum minimization of singular values in robust pca algorithm and applications"

Connected through hyperedge HE-8 (induced by source paper "automated spectral kernel learning"):
  with members:
    - "functional variational bayesian neural networks"
    - "orthogonal random features"
    - "adam a method for stochastic optimization"
    - "fastfood approximate kernel expansions in loglinear time"
    - "large scale multi label learning with missing labels"
    - "adadelta an adaptive learning rate method"

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)