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: "tiered forensic methodology model for digital field triage by non digital evidence specialists"
  Abstract: Due to budgetary constraints and the high level of training required, digital forensic analysts are in short supply in police forces the world over. This inevitably leads to a prolonged time taken between an investigator sending the digital evidence for analysis and receiving the analytical report back. In an attempt to expedite this procedure, various process models have been created to place the forensic analyst in the field conducting a triage of the digital evidence. By conducting triage in the field, an investigator is able to act upon pertinent information quicker, while waiting on the full report.The work presented as part of this paper focuses on the training of front-line personnel in the field triage process, without the need of a forensic analyst attending the scene. The premise has been successfully implemented within regular/non-digital forensics, i.e., crime scene investigation. In that field, front-line members have been trained in specific tasks to supplement the trained specialists. The concept of front-line members conducting triage of digital evidence in the field is achieved through the development of a new process model providing guidance to these members. To prove the model's viability, an implementation of this new process model is presented and evaluated. The results outlined demonstrate how a tiered response involving digital evidence specialists and non-specialists can better deal with the increasing number of investigations involving digital evidence.

Connected through hyperedge HE-1 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts"):
  with members:
    - "deep learning at the shallow end malware classification for non domain experts"
    - "increasing digital investigator availability through efficient workflow management and automation"
    - "evaluation of digital forensic process models with respect to digital forensics as a service"
    - "battling the digital forensic backlog through data deduplication"
    - "current challenges and future research areas for digital forensic investigation"

Connected through hyperedge HE-2 (induced by source paper "deep learning at the shallow end malware classification for non domain experts"):
  with members:
    - "microsoft malware classification challenge"
    - "network intell enabling the non expert analysis of large volumes of intercepted network traffic"
    - "recent trends in deep learning based natural language processing"
    - "increasing digital investigator availability through efficient workflow management and automation"
    - "integration of ether unpacker into ragpicker for plugin based malware analysis and identification"
    - "battling the digital forensic backlog through data deduplication"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "enabling trust in deep learning models a digital forensics case study"):
  with members:
    - "cloudme forensics a case of big data forensic investigation"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"
    - "axiomatic attribution for deep networks"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "forensics acquisition and analysis of instant messaging and voip applications"
    - "toward an efficient website fingerprinting defense"

Connected through hyperedge HE-6 (induced by source paper "battling the digital forensic backlog through data deduplication"):
  with members:
    - "increasing digital investigator availability through efficient workflow management and automation"
    - "current challenges and future research areas for digital forensic investigation"
    - "automated inference of past action instances in digital investigations"
    - "leveraging decentralization to extend the digital evidence acquisition window case study on bittorrent sync"

Connected through hyperedge HE-7 (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"
    - "network investigation methodology for bittorrent sync"
    - "challenges with automation in digital forensic investigations"

Connected through hyperedge HE-8 (induced by source paper "current challenges and future research areas for digital forensic investigation"):
  with members:
    - "using graphics processors for parallelizing hash based data carving"

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)