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: "global variational method for fingerprint segmentation by three part decomposition"
  Abstract: Verifying an identity claim by fingerprint recognition is a commonplace experience for millions of people in their daily life, for example, for unlocking a tablet computer or smartphone. The first processing step after fingerprint image acquisition is segmentation, that is, dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region. The authors propose a novel segmentation method by global three-part decomposition (G3PD). On the basis of global variational analysis, the G3PD method decomposes a fingerprint image into cartoon, texture and noise parts. After decomposition, the foreground region is obtained from the non-zero coefficients in the texture image using morphological processing. The segmentation performance of the G3PD method is compared with five state-of-the-art methods on a benchmark which comprises manually marked ground truth segmentation for 10,560 images. Performance evaluations show that the G3PD method consistently outperforms existing methods in terms of segmentation accuracy.

Connected through hyperedge HE-1 (induced by source paper "perfect fingerprint orientation fields by locally adaptive global models"):
  with members:
    - "directional global three part image decomposition"
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "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:
    - "directional global three part image 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 "directional global three part image decomposition"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "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"
    - "imaging with kantorovich rubinstein discrepancy"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-4 (induced by source paper "simultaneous inpainting and denoising by directional global three part decomposition connecting variational and fourier domain based image processing"):
  with members:
    - "directional global three part image decomposition"
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-5 (induced by source paper "multiphase segmentation for simultaneously homogeneous and textural images"):
  with members:
    - "directional global three part image decomposition"
    - "filter design and performance evaluation for fingerprint image segmentation"

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)