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: "identifying user behavior from residual data in cloud based synchronized apps"
  Abstract: As the distinction between personal and organizational device usage continues to blur, the combination of applications that interact increases the need to investigate potential security issues. Although security and forensic researchers have been able to recover a variety of artifacts, empirical research has not examined a suite of application artifacts from the perspective of high-level pattern identification. This research presents a preliminary investigation into the idea that residual artifacts generated by cloud-based synchronized applications can be used to identify broad user behavior patterns. To accomplish this, the researchers conducted a single-case, pretest-posttest, quasi experiment using a smartphone device and a suite of Google mobile applications. The contribution of this paper is two-fold. First, it provides a proof of concept of the extent to which residual data from cloud-based synchronized applications can be used to broadly identify user behavior patterns from device data patterns. Second, it highlights the need for security controls to prevent and manage information flow between BYOD mobile devices and cloud synchronization services. #R##N#Keywords: Residual Data, Cloud, Apps, Digital Forensics, BYOD

Connected through hyperedge HE-1 (induced by source paper "digital blues an investigation into the use of bluetooth protocols"):
  with members:
    - "security threats analysis in bluetooth enabled mobile devices"

Connected through hyperedge HE-2 (induced by source paper "recovering residual forensic data from smartphone interactions with cloud storage providers"):
  with members:
    - "digital forensic investigation of cloud storage services"
    - "emerging security challenges of cloud virtual infrastructure"
    - "using smartphones as a proxy for forensic evidence contained in cloud storage services"
    - "cloud security challenges investigating policies standards and guidelines in a fortune 500 organization"
    - "calm before the storm the challenges of cloud computing in digital forensics"

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)