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: "screen content image segmentation using sparse smooth decomposition"
  Abstract: Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.

Connected through hyperedge HE-1 (induced by source paper "enhanced signal recovery via sparsity inducing image priors"):
  with members:
    - "sparsity based color image super resolution via exploiting cross channel constraints"
    - "deep convolutional neural network for inverse problems in imaging"
    - "accelerating the super resolution convolutional neural network"
    - "detecting dominant vanishing points in natural scenes with application to composition sensitive image retrieval"
    - "accurate image super resolution using very deep convolutional networks"
    - "self tuned deep super resolution"
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "subspace learning in the presence of sparse structured outliers and noise"):
  with members:
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "robust computation of linear models by convex relaxation"
    - "screen content image segmentation using least absolute deviation fitting"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-3 (induced by source paper "towards coordinated bandwidth adaptations for hundred scale 3d tele immersive systems"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "screen content image segmentation using least absolute deviation fitting"

Connected through hyperedge HE-4 (induced by source paper "sparsity based color image super resolution via exploiting cross channel constraints"):
  with members:
    - "screen content image segmentation using least absolute deviation fitting"
    - "a bayesian nonparametric approach to image super resolution"

Connected through hyperedge HE-5 (induced by source paper "screen content image segmentation using sparse decomposition and total variation minimization"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "screen content image segmentation using least absolute deviation fitting"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

Connected through hyperedge HE-6 (induced by source paper "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"):
  with members:
    - "innovation pursuit a new approach to subspace clustering"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "screen content image segmentation using least absolute deviation fitting"
    - "performance guarantees for reprocs correlated low rank matrix entries case"
    - "a subspace method for array covariance matrix estimation"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressive principal component pursuit"

Connected through hyperedge HE-7 (induced by source paper "fingerprint recognition using translation invariant scattering network"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"
    - "classification with scattering operators"

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)