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: "securing fingerprint template using noninvertible ridge feature transformation"
  Abstract: In a biometric verification system, leakage of biometric data leads to permanent identity loss since original biometric data is inherently linked to a user. Further, various types of attacks on a biometric system may reveal the original template and utility in other applications. To address these security and privacy concerns cancelable biometric has been introduced. Cancelable biometric constructs a protected template from the original biometric template using transformation functions and performs the comparison between templates in the transformed domain. Recent approaches towards cancelable fingerprint generation either rely on aligning minutiae points with respect to singular points (core/delta) or utilize the absolute coordinate positions of minutiae points. In this paper, we propose a novel non-invertible ridge feature transformation method to protect the original fingerprint template information. The proposed method partitions the fingerprint region into a number of sectors with reference to each minutia point employing a ridge-based co-ordinate system. The nearest neighbor minutiae in each sector are identified, and ridge-based features are computed. Further, a cancelable template is generated by applying the Cantor pairing function followed by random projection. We have evaluated our method with FVC2002, FVC2004 and FVC2006 databases. It is evident from the experimental results that the proposed method outperforms existing methods in the literature. Moreover, the security analysis demonstrates that the proposed method fulfills the necessary requirements of non-invertibility, revocability, and diversity with a minor performance degradation caused due to cancelable transformation.

Connected through hyperedge HE-1 (induced by source paper "a novel hybrid score level and decision level fusion scheme for cancelable multi biometric verification"):
  with members:
    - "multibiometric secure system based on deep learning"

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)