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 least absolute deviation fitting"
  Abstract: We propose an algorithm for separating the foreground (mainly text and line graphics) from the smoothly varying background in screen content images. 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 create sharp discontinuity and cannot be modeled by this smooth representation. The algorithm separates the background and foreground using a least absolute deviation method to fit the smooth model to the image pixels. This algorithm has been tested on several images from HEVC standard test sequences for screen content coding, and is shown to have superior performance over other popular methods, such as k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm. Such background/foreground segmentation are important pre-processing steps for text extraction and separate coding of background and foreground for compression of screen content images.

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"
    - "screen content image segmentation using sparse smooth decomposition"
    - "accurate image super resolution using very deep convolutional networks"
    - "self tuned deep super resolution"
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"

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"
    - "screen content image segmentation using sparse smooth decomposition"
    - "robust computation of linear models by convex relaxation"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-3 (induced by source paper "masked signal decomposition using subspace representation and its applications"):
  with members:
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "tilt transform invariant low rank textures"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "towards coordinated bandwidth adaptations for hundred scale 3d tele immersive systems"):
  with members:
    - "screen content image segmentation using sparse smooth decomposition"
    - "iris recognition using scattering transform and textural features"

Connected through hyperedge HE-5 (induced by source paper "cloud based deep learning of big eeg data for epileptic seizure prediction"):
  with members:
    - "fingerprint recognition using translation invariant scattering network"
    - "union of low rank subspaces detector"

Connected through hyperedge HE-6 (induced by source paper "sparsity based color image super resolution via exploiting cross channel constraints"):
  with members:
    - "screen content image segmentation using sparse smooth decomposition"
    - "a bayesian nonparametric approach to image super resolution"

Connected through hyperedge HE-7 (induced by source paper "palmprint recognition using deep scattering convolutional network"):
  with members:
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "fingerprint recognition using translation invariant scattering network"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-8 (induced by source paper "screen content image segmentation using robust regression and sparse decomposition"):
  with members:
    - "learning efficient sparse and low rank models"
    - "stable image reconstruction using total variation minimization"
    - "tilt transform invariant low rank textures"
    - "robust principal component analysis"

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)