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: "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"
  Abstract: Gabor filters (GFs) play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved GFs that locally adapt their shape to the direction of flow. These curved GFs enable the choice of filter parameters that increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved GFs are applied to the curved ridge and valley structures of low-quality fingerprint images. First, we combine two orientation-field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation. Subsequently, these curved regions are used for estimating the local ridge frequency. Finally, curved GFs are defined based on curved regions, and they apply the previously estimated orientations and ridge frequencies for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison with state-of-the-art enhancement methods.

Connected through hyperedge HE-1 (induced by source paper "two stage quality adaptive fingerprint image enhancement using fuzzy c means clustering based fingerprint quality analysis"):
  with members:
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "filter design and performance evaluation for fingerprint image segmentation"):
  with members:
    - "directional global three part image decomposition"
    - "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"
    - "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-4 (induced by source paper "directional global three part image decomposition"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "global variational method for fingerprint segmentation by three part 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"

Connected through hyperedge HE-6 (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"
    - "global variational method for fingerprint segmentation by three part decomposition"

Connected through hyperedge HE-7 (induced by source paper "the filament sensor for near real time detection of cytoskeletal fiber structures"):
  with members:
    - "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems"
    - "a multi orientation analysis approach to retinal vessel tracking"

Connected through hyperedge HE-8 (induced by source paper "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems"):
  with members:
    - "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
    - "modeling the growth of fingerprints improves matching for adolescents"

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)