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: "privacy preserving internet browsers forensic analysis of browzar"
  Abstract: With the advance of technology, Criminal Justice agencies are being confronted with an increased need to investigate crimes perpetuated partially or entirely over the Internet. These types of crime are known as cybercrimes. In order to conceal illegal online activity, criminals often use private browsing features or browsers designed to provide total browsing privacy. The use of private browsing is a common challenge faced in for example child exploitation investigations, which usually originate on the Internet. Although private browsing features are not designed specifically for criminal activity, they have become a valuable tool for criminals looking to conceal their online activity. As such, Technological Crime units often focus their forensic analysis on thoroughly examining the web history on a computer. Private browsing features and browsers often require a more in-depth, post mortem analysis. This often requires the use of multiple tools, as well as different forensic approaches to uncover incriminating evidence. This evidence may be required in a court of law, where analysts are often challenged both on their findings and on the tools and approaches used to recover evidence. However, there are very few research on evaluating of private browsing in terms of privacy preserving as well as forensic acquisition and analysis of privacy preserving internet browsers. Therefore in this chapter, we firstly review the private mode of popular internet browsers. Next, we describe the forensic acquisition and analysis of Browzar, a privacy preserving internet browser and compare it with other popular internet browsers

Connected through hyperedge HE-1 (induced by source paper "privileged data within digital evidence"):
  with members:
    - "a bespoke forensics gis tool"
    - "battling the digital forensic backlog through data deduplication"
    - "forensics acquisition and analysis of instant messaging and voip applications"

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)