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 "fixed length bit string representation of fingerprint by normalized local structures"):
  Abstract: In this paper, we propose a method to represent a fingerprint image by an ordered, fixed-length bit-string providing improved accuracy performance, faster matching time and compressibility. First, we devise a novel minutia-based local structure modeled by a mixture of 2D elliptical Gaussian functions in the pixel space. Each local structure is mapped to the Euclidean space by normalizing the local structure with the number of minutiae that associates to it. This simple yet crucial crux enables fast dissimilarity computation of two local structures with Euclidean distance without distortion. A complementary texture-based local structure to the minutia-based local structure is also introduced whereby both can be compressed via principal component analysis and fused easily in the Euclidean space. The fused local structure is then converted to a K-bit ordered string via a K-means clustering algorithm. This chain of computation with sole use of Euclidean distance is vital for speedy and discriminative bit-string conversion. The accuracy can be further improved by a finger-specific bit-training algorithm in which two criteria are leveraged to select useful bit positions for matching. Experiments are performed on Fingerprint Verification Competition (FVC) databases for comparison with existing techniques to show the superiority of the proposed method.

Includes members:
  Member "ranking based locality sensitive hashing enabled cancelable biometrics index of max hashing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a genetic algorithm enabled similarity based attack on cancellable biometrics")
  Member "efficient compression technique for sparse sets":
    also connected through other hyperedges:
      (none)
  Member "similarity preserving compressions of high dimensional sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient compression technique for sparse sets")

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)