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: "filter design and performance evaluation for fingerprint image segmentation"
  Abstract: Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

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"
    - "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 "directional global three part image decomposition"):
  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"
    - "imaging with kantorovich rubinstein discrepancy"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-3 (induced by source paper "global variational method for fingerprint segmentation by three part decomposition"):
  with members:
    - "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"

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"
    - "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-5 (induced by source paper "multiphase segmentation for simultaneously homogeneous and textural images"):
  with members:
    - "directional global three part image decomposition"
    - "global variational method for fingerprint segmentation by three part decomposition"

Connected through hyperedge HE-6 (induced by source paper "skilled impostor attacks against fingerprint verification systems and its remedy"):
  with members:
    - "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems"

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)