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: "forensics acquisition and analysis of instant messaging and voip applications"
  Abstract: The advent of the Internet has significantly transformed the daily activities of millions of people, with one of them being the way people communicate where Instant Messaging (IM) and Voice over IP (VoIP) communications have become prevalent. Although IM applications are ubiquitous communication tools nowadays, it was observed that the relevant research on the topic of evidence collection from IM services was limited. The reason is an IM can serve as a very useful yet very dangerous platform for the victim and the suspect to communicate. Indeed, the increased use of Instant Messengers on smart phones has turned to be the goldmine for mobile and computer forensic experts. Traces and Evidence left by applications can be held on smart phones and retrieving those potential evidences with right forensic technique is strongly required. Recently, most research on IM forensics focus on applications such as WhatsApp, Viber and Skype. However, in the literature, there are very few forensic analysis and comparison related to IM applications such as WhatsApp, Viber and Skype and Tango on both iOS and Android platforms, even though the total users of this application already exceeded 1 billion. Therefore, in this paper we present forensic acquisition and analysis of these four IMs and VoIPs for both iOS and Android platforms. We try to answer on how evidence can be collected when IM communications are used. We also define taxonomy of target artefacts in order to guide and structure the subsequent forensic analysis. Finally, a review of the information that can become available via the IM vendor was conducted. The achieved results of this research provided elaborative answers on the types of artifacts that can be identified by these IM and VoIP applications. We compare moreover the forensics analysis of these popular applications: WhatApp, Skype, Viber and Tango.

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

Connected through hyperedge HE-2 (induced by source paper "privacy preserving internet browsers forensic analysis of browzar"):
  with members:
    - "an efficient search tool for an anti money laundering application of an multi national bank s dataset"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "toward an efficient website fingerprinting defense"

Connected through hyperedge HE-3 (induced by source paper "privileged data within digital evidence"):
  with members:
    - "privacy preserving internet browsers forensic analysis of browzar"
    - "a bespoke forensics gis tool"
    - "battling the digital forensic backlog through data deduplication"

Connected through hyperedge HE-4 (induced by source paper "windows instant messaging app forensics facebook and skype as case studies"):
  with members:
    - "a forensically sound adversary model for mobile devices"
    - "forensic taxonomy of popular android mhealth apps"
    - "privacy risks in mobile dating apps"
    - "an analysis of the skype peer to peer internet telephony protocol"

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)