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: "an empirical methodology to evaluate vulnerability discovery models"
  Abstract: Vulnerability discovery models (VDMs) operate on known vulnerability data to estimate the total number of vulnerabilities that will be reported after a software is released. VDMs have been proposed by industry and academia, but there has been no systematic independent evaluation by researchers who are not model proponents. Moreover, the traditional evaluation methodology has some issues that biased previous studies in the field. In this work we propose an empirical methodology that systematically evaluates the performance of VDMs along two dimensions (quality and predictability) and addresses all identified issues of the traditional methodology. We conduct an experiment to evaluate most existing VDMs on popular web browsers’ vulnerability data. Our comparison shows that the results obtained by the proposed methodology are more informative than those by the traditional methodology. Among evaluated VDMs, the simplest linear model is the most appropriate choice in terms of both quality and predictability for the first 6-12 months since a release date. Otherwise, logistics-based models are better choices.

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.CR(Cryptography and Security)