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: "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
  Abstract: In this study we show that by the current state-of-the-art synthetically generated ngerprints can easily be discriminated from real ngerprints. We propose a method based on second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a xed-length feature vector for a ngerprint which are invariant under rotation and translation. This ’test of realness’ can be applied to synthetic ngerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of ngerprint recognition algorithms; 3 of these 12 databases consist of articial ngerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic ngerprints generated by the software of Bicz versus real ngerprint images. We conclude with suggestions for the improvement of synthetic ngerprint generation.

Connected through hyperedge HE-1 (induced by source paper "fingerprint synthesis search with 100 million prints"):
  with members:
    - "learning a fixed length fingerprint representation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "improved training of wasserstein gans"
    - "deepmasterprints generating masterprints for dictionary attacks via latent variable evolution"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

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"
    - "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-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"
    - "imaging with kantorovich rubinstein discrepancy"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

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"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through 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"):
  with members:
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"
    - "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)