human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "filter design and performance evaluation for fingerprint image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perfect fingerprint orientation fields by locally adaptive global models")
  Member "global variational method for fingerprint segmentation by three part decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perfect fingerprint orientation fields by locally adaptive global models")
  Member "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low complexity data parallel earth mover s distance approximations")
  Member "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fingerprint synthesis search with 100 million prints")
  Member "imaging with kantorovich rubinstein discrepancy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convex histogram based joint image segmentation with regularized optimal transport cost")
  Member "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two stage quality adaptive fingerprint image enhancement using fuzzy c means clustering based fingerprint quality analysis")

Question: Please tell me which class the source paper 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)