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: "network investigation methodology for bittorrent sync"
  Abstract: High availability is no longer just a business continuity concern. Users are increasingly dependant on devices that consume and produce data in ever increasing volumes. A popular solution is to have a central repository which each device accesses after centrally managed authentication. This model of use is facilitated by cloud based file synchronisation services such as Dropbox, OneDrive, Google Drive and Apple iCloud. Cloud architecture allows the provisioning of storage space with "always-on" access. Recent concerns over unauthorised access to third party systems and large scale exposure of private data have made an alternative solution desirable. These events have caused users to assess their own security practices and the level of trust placed in third party storage services. One option is BitTorrent Sync, a cloudless synchronisation utility provides data availability and redundancy. This utility replicates files stored in shares to remote peers with access controlled by keys and permissions. While lacking the economies brought about by scale, complete control over data access has made this a popular solution. The ability to replicate data without oversight introduces risk of abuse by users as well as difficulties for forensic investigators. This paper suggests a methodology for investigation and analysis of the protocol to assist in the control of data flow across security perimeters.

Connected through hyperedge HE-1 (induced by source paper "forensic investigation of p2p cloud storage bittorrent sync as a case study"):
  with members:
    - "ubuntu one investigation detecting evidences on client machines"
    - "digital forensic investigation of cloud storage services"
    - "windows instant messaging app forensics facebook and skype as case studies"
    - "mobile cloud forensics an analysis of seven popular android apps"
    - "leveraging decentralization to extend the digital evidence acquisition window case study on bittorrent sync"
    - "bittorrent sync first impressions and digital forensic implications"
    - "bittorrent sync network investigation methodology"
    - "seclaas secure logging as a service for cloud forensics"

Connected through hyperedge HE-2 (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"
    - "forensics acquisition and analysis of instant messaging and voip applications"
    - "project maelstrom forensic analysis of the bittorrent powered browser"

Connected through hyperedge HE-3 (induced by source paper "towards the leveraging of data deduplication to break the disk acquisition speed limit"):
  with members:
    - "battling the digital forensic backlog through data deduplication"
    - "current challenges and future research areas for digital forensic investigation"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "challenges with automation in digital forensic investigations"

Connected through hyperedge HE-4 (induced by source paper "project maelstrom forensic analysis of the bittorrent powered browser"):
  with members:
    - "leveraging decentralization to extend the digital evidence acquisition window case study on 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)