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 "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.

Includes members:
  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-1 (induced by source paper "low complexity data parallel earth mover s distance approximations")
  Member "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "filter design and performance evaluation for fingerprint image segmentation")

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)