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: "directional global three part image decomposition"
  Abstract: We consider the task of image decomposition, and we introduce a new model coined directional global three-part decomposition (DG3PD) for solving it. As key ingredients of the DG3PD model, we introduce a discrete multi-directional total variation norm and a discrete multi-directional G-norm. Using these novel norms, the proposed discrete DG3PD model can decompose an image into two or three parts. Existing models for image decomposition by Vese and Osher (J. Sci. Comput. 19(1–3):553–572, 2003), by Aujol and Chambolle (Int. J. Comput. Vis. 63(1):85–104, 2005), by Starck et al. (IEEE Trans. Image Process. 14(10):1570–1582, 2005), and by Thai and Gottschlich are included as special cases in the new model. Decomposition of an image by DG3PD results in a cartoon image, a texture image, and a residual image. Advantages of the DG3PD model over existing ones lie in the properties enforced on the cartoon and texture images. The geometric objects in the cartoon image have a very smooth surface and sharp edges. The texture image yields oscillating patterns on a defined scale which are both smooth and sparse. Moreover, the DG3PD method achieves the goal of perfect reconstruction by summation of all components better than the other considered methods. Relevant applications of DG3PD are a novel way of image compression as well as feature extraction for applications such as latent fingerprint processing and optical character recognition.

Connected through hyperedge HE-1 (induced by source paper "perfect fingerprint orientation fields by locally adaptive global models"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "global variational method for fingerprint segmentation by three part decomposition"
    - "the filament sensor for near real time detection of cytoskeletal fiber structures"
    - "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-2 (induced by source paper "filter design and performance evaluation for fingerprint image segmentation"):
  with members:
    - "global variational method for fingerprint segmentation by three part decomposition"
    - "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems"
    - "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"
    - "modeling the growth of fingerprints improves matching for adolescents"
    - "construction of hilbert transform pairs of wavelet bases and gabor like transforms"

Connected through hyperedge HE-3 (induced by source paper "simultaneous inpainting and denoising by directional global three part decomposition connecting variational and fourier domain based image processing"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "global variational method for fingerprint segmentation by three part decomposition"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-4 (induced by source paper "multiphase segmentation for simultaneously homogeneous and textural images"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "global variational method for fingerprint segmentation by three part decomposition"

Connected through hyperedge HE-5 (induced by source paper "directional mean curvature for textured image demixing"):
  with members:
    - "multiphase segmentation for simultaneously homogeneous and textural images"

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)