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: "nonlocal low rank tensor factor analysis for image restoration"
  Abstract: Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications. We propose a new method that employs low-rank tensor factor analysis for tensors generated by grouped image patches. The low-rank tensors are fed into the alternative direction multiplier method (ADMM) to further improve image reconstruction. The motivating application is compressive sensing (CS), and a deep convolutional architecture is adopted to approximate the expensive matrix inversion in CS applications. An iterative algorithm based on this low-rank tensor factorization strategy, called NLR-TFA, is presented in detail. Experimental results on noiseless and noisy CS measurements demonstrate the superiority of the proposed approach, especially at low CS sampling rates.

Connected through hyperedge HE-1 (induced by source paper "robust subspace discovery by block diagonal adaptive locality constrained representation"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "subspace clustering by block diagonal representation"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "non local meets global an integrated paradigm for hyperspectral denoising"):
  with members:
    - "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
    - "taking human out of learning applications a survey on automated machine learning"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "robust low rank matrix factorization under general mixture noise distributions"

Connected through hyperedge HE-3 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg"):
  with members:
    - "block wise lensless compressive camera"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "tree structure bayesian compressive sensing for video"
    - "from denoising to compressed sensing"
    - "compressive measurements generated by structurally random matrices asymptotic normality and quantization"

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)