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 "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.

Includes members:
  Member "directional global three part image decomposition":
    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 "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two stage quality adaptive fingerprint image enhancement using fuzzy c means clustering based fingerprint quality analysis")
  Member "modeling the growth of fingerprints improves matching for adolescents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems")
  Member "construction of hilbert transform pairs of wavelet bases and gabor like transforms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a panorama on multiscale geometric representations intertwining spatial directional and frequency selectivity")

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)