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: "evihunter identifying digital evidence in the permanent storage of android devices via static analysis"
  Abstract: Crimes, both physical and cyber, increasingly involve smartphones due to their ubiquity. Therefore, digital evidence on smartphones plays an increasingly important role in crime investigations. Digital evidence could reside in the memory and permanent storage of a smartphone. While we have witnessed significant progresses on memory forensics recently, identifying evidence in the permanent storage is still an underdeveloped research area. Most existing studies on permanent-storage forensics rely on manual analysis or keyword-based scanning of the permanent storage. Manual analysis is costly, while keyword matching often misses the evidentiary data that do not have interesting keywords. #R##N#In this work, we develop a tool called EviHunter to automatically identify evidentiary data in the permanent storage of an Android device. There could be thousands of files on the permanent storage of a smartphone. A basic question a forensic investigator often faces is which files could store evidentiary data. EviHunter aims to answer this question. Our intuition is that the evidentiary data were produced by apps; and an app's code has rich information about the types of data the app may write to a permanent storage and the files the data are written to. Therefore, EviHunter first pre-computes an App Evidence Database (AED) via static analysis of a large number of apps. The AED includes the types of evidentiary data and files that store them for each app. Then, EviHunter matches the files on a smartphone's permanent storage against the AED to identify the files that could store evidentiary data. #R##N#We evaluate EviHunter on benchmark apps and 8,690 real-world apps. Our results show that EviHunter can precisely identify both the types of evidentiary data and the files that store them.

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)