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 decomposition and total variation minimization"
  Abstract: Sparse decomposition has been widely used for different applications, such as source separation, image classification, image denoising and more. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition and total variation minimization. The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics can be modeled with a sparse component overlaid on the smooth background. The background and foreground are separated using a sparse decomposition framework regularized with a few suitable regularization terms which promotes the sparsity and connectivity of foreground pixels. This algorithm has been tested on a dataset of images extracted from HEVC standard test sequences for screen content coding, and is shown to have superior performance over some prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm.

Connected through hyperedge HE-1 (induced by source paper "text extraction from texture images using masked signal decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "text flow a unified text detection system in natural scene images"
    - "textproposals a text specific selective search algorithm for word spotting in the wild"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a hough transform based technique for text segmentation"

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 smooth decomposition"
    - "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 "robust and scalable column row sampling from corrupted big data"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "innovation pursuit a new approach to subspace clustering"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "dissimilarity based sparse subset selection"

Connected through hyperedge HE-4 (induced by source paper "palmprint recognition using deep scattering convolutional network"):
  with members:
    - "fingerprint recognition using translation invariant scattering network"
    - "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"

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)