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: "forensic analysis of epic privacy browser on windows operating systems"
  Abstract: Internet security can be compromised not only through the threat of Malware, fraud, system intrusion or damage, but also via the tracking of internet activity. Criminals are using numerous methods to access data in the highly lucrative cybercrime business. Organized crime, as well as individual users, are benefiting from the protection of Virtual Private Networks (VPN) and private browsers, such as Tor, Epic Privacy, to carry out illegal activity such as money laundering, drug dealing and the trade of child pornography. News articles advising on internet privacy assisted in educating the public and a new era of private browsing arose. Although these measures were designed to protect legitimate browsing privacy, they also provided a means to conceal illegal activity. One such tool released for private browsing was Epic Privacy Browser. It is currently used in approximately 180 countries worldwide. Epic Privacy Browser is promoted as a chromium powered browser, specifically engineered to protect users' privacy. It only operates in private browser mode and, upon close of the browsing session, deletes all browsing data. The Epic Privacy Browser claims that all traces of user activity will be cleared upon close of the application and will establish if the introduction of Windows 10 has had an adverse effect on this claim. However, there is no forensic acquisition and analysis of Epic Privacy Browser in literature. In this paper, we aim to contribute towards the goal of assisting forensic examiners with the locations and types of evidence available through live and post-mortem state analysis of the Epic Privacy Browser on Windows 10 and Windows 7, identify how the browser functions during use, where data can be recovered once the browser is closed and the necessary tools that will assist in the forensics discovery and effective presentation of the material.

Connected through hyperedge HE-1 (induced by source paper "private web browser forensics a case study of the epic privacy browser"):
  with members:
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "forensics acquisition and analysis of instant messaging and voip applications"
    - "project maelstrom forensic analysis of the bittorrent powered browser"
    - "network investigation methodology for bittorrent sync"

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)