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 "perfect fingerprint orientation fields by locally adaptive global models"):
  Abstract: Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this study, a novel approach is presented to globally model an OF combined with locally adaptive methods. The authors show that this model adapts perfectly to the ‘true OF’ in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high-fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution, they describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and they establish the main underlying claim of high-fidelity low parameter OF compression.

Includes members:
  Member "directional global three part image decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "filter design and performance evaluation for fingerprint image segmentation")
  Member "filter design and performance evaluation for fingerprint image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "directional global three part image decomposition")
  Member "global variational method for fingerprint segmentation by three part decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "filter design and performance evaluation for fingerprint image segmentation")
  Member "the filament sensor for near real time detection of cytoskeletal fiber structures":
    also connected through other hyperedges:
      (none)
  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")

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)