human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts"):
  Abstract: The ever increasing volume of data in digital forensic investigation is one of the most discussed challenges in the field. Usually, most of the file artefacts on seized devices are not pertinent to the investigation. Manually retrieving suspicious files relevant to the investigation is akin to finding a needle in a haystack. In this paper, a methodology for the automatic prioritisation of suspicious file artefacts (i.e., file artefacts that are pertinent to the investigation) is proposed to reduce the manual analysis effort required. This methodology is designed to work in a human-in-the-loop fashion. In other words, it predicts/recommends that an artefact is likely to be suspicious rather than giving the final analysis result. A supervised machine learning approach is employed, which leverages the recorded results of previously processed cases. The process of features extraction, dataset generation, training and evaluation are presented in this paper. In addition, a toolkit for data extraction from disk images is outlined, which enables this method to be integrated with the conventional investigation process and work in an automated fashion.

Includes members:
  Member "deep learning at the shallow end malware classification for non domain experts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "feature level malware obfuscation in deep learning")
  Member "increasing digital investigator availability through efficient workflow management and automation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning at the shallow end malware classification for non domain experts")
  Member "evaluation of digital forensic process models with respect to digital forensics as a service":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of electromagnetic side channel attacks and discussion on their case progressing potential for digital forensics")
  Member "battling the digital forensic backlog through data deduplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning at the shallow end malware classification for non domain experts")
  Member "current challenges and future research areas for digital forensic investigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a compression based framework for the detection of anomalies in heterogeneous data sources")
  Member "tiered forensic methodology model for digital field triage by non digital evidence specialists":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning at the shallow end malware classification for non domain experts")

Question: Please tell me which class the source paper 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)