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: "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
  Abstract: This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust PCA (Candes et al. 2011) to the tensor case. Our model is based on a new tensor Singular Value Decomposition (t-SVD) (Kilmer and Martin 2011) and its induced tensor tubal rank and tensor nuclear norm. Consider that we have a 3-way tensor ${\mathcal{X}}\in\mathbb{R}^{n_1\times n_2\times n_3}$ such that ${\mathcal{X}}={\mathcal{L}}_0+{\mathcal{E}}_0$, where ${\mathcal{L}}_0$ has low tubal rank and ${\mathcal{E}}_0$ is sparse. Is that possible to recover both components? In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the $\ell_1$-norm, i.e., $\min_{\mathcal{L},\ {\mathcal{E}}} \ \|{\mathcal{L}}\|_*+\lambda\|{\mathcal{E}}\|_1, \ \text{s.t.} \ {\mathcal{X}}={\mathcal{L}}+{\mathcal{E}}$, where $\lambda= {1}/{\sqrt{\max(n_1,n_2)n_3}}$. Interestingly, TRPCA involves RPCA as a special case when $n_3=1$ and thus it is a simple and elegant tensor extension of RPCA. Also numerical experiments verify our theory and the application for the image denoising demonstrates the effectiveness of our method.

Connected through hyperedge HE-1 (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"
    - "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"
    - "generalized singular value thresholding"

Connected through hyperedge HE-2 (induced by source paper "generalized visual information analysis via tensorial algebra"):
  with members:
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "the power and arnoldi methods in an algebra of circulants"

Connected through hyperedge HE-3 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "iterative collaborative filtering for sparse noisy tensor estimation"
    - "fundamental conditions for low cp rank tensor completion"
    - "exact tensor completion with sum of squares"
    - "on the optimization landscape of tensor decompositions"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "low rank tensor recovery via iterative hard thresholding"

Connected through hyperedge HE-4 (induced by source paper "low rank approximation from communication complexity"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "parameterized low rank binary matrix approximation"
    - "relative error tensor low rank approximation"
    - "low rank passthrough neural networks"
    - "greedy bi criteria approximations for k medians and k means"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"

Connected through hyperedge HE-5 (induced by source paper "tensor graph convolutional networks for prediction on dynamic graphs"):
  with members:
    - "dynamic graph convolutional networks"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "stable tensor neural networks for rapid deep learning"
    - "community interaction and conflict on the web"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-6 (induced by source paper "deep plug and play prior for low rank tensor completion"):
  with members:
    - "plug and play methods provably converge with properly trained denoisers"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "low rank tensor completion for multiway visual data"
    - "regularization by denoising clarifications and new interpretations"
    - "non local meets global an integrated paradigm for hyperspectral denoising"
    - "tensor n tubal rank and its convex relaxation for low rank tensor recovery"
    - "image restoration by iterative denoising and backward projections"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-7 (induced by source paper "robust tensor completion using transformed tensor svd"):
  with members:
    - "exact tensor completion from sparsely corrupted observations via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "parallel matrix factorization for low rank tensor completion"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "spectral unmixing via data guided sparsity"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-8 (induced by source paper "color image and multispectral image denoising using block diagonal representation"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "renoir a dataset for real low light image noise reduction"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

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)