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: "current challenges and future research areas for digital forensic investigation"
  Abstract: Given the ever-increasing prevalence of technology in modern life, there is a corresponding increase in the likelihood of digital devices being pertinent to a criminal investigation or civil litigation. As a direct consequence, the number of investigations requiring digital forensic expertise is resulting in huge digital evidence backlogs being encountered by law enforcement agencies throughout the world. It can be anticipated that the number of cases requiring digital forensic analysis will greatly increase in the future. It is also likely that each case will require the analysis of an increasing number of devices including computers, smartphones, tablets, cloud-based services, Internet of Things devices, wearables, etc. The variety of new digital evidence sources pose new and challenging problems for the digital investigator from an identification, acquisition, storage and analysis perspective. This paper explores the current challenges contributing to the backlog in digital forensics from a technical standpoint and outlines a number of future research topics that could greatly contribute to a more efficient digital forensic process.

Connected through hyperedge HE-1 (induced by source paper "a compression based framework for the detection of anomalies in heterogeneous data sources"):
  with members:
    - "an adaptive system for detecting malicious queries in web attacks"
    - "predicting cyber events by leveraging hacker sentiment"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-2 (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"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"

Connected through hyperedge HE-3 (induced by source paper "a survey of electromagnetic side channel attacks and discussion on their case progressing potential for digital forensics"):
  with members:
    - "systematic classification of side channel attacks a case study for mobile devices"
    - "watch me but don t touch me contactless control flow monitoring via electromagnetic emanations"
    - "evaluation of digital forensic process models with respect to digital forensics as a service"
    - "full duplex backscatter interference networks based on time hopping spread spectrum"

Connected through hyperedge HE-4 (induced by source paper "improving borderline adulthood facial age estimation through ensemble learning"):
  with members:
    - "deep learning at the shallow end malware classification for non domain experts"
    - "age progression regression by conditional adversarial autoencoder"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-5 (induced by source paper "leveraging electromagnetic side channel analysis for the investigation of iot devices"):
  with members:
    - "a survey of electromagnetic side channel attacks and discussion on their case progressing potential for digital forensics"
    - "evaluation of digital forensic process models with respect to digital forensics as a service"

Connected through hyperedge HE-6 (induced by source paper "cybercrime investigators are users too understanding the socio technical challenges faced by law enforcement"):
  with members:
    - "cybercrime and you how criminals attack and the human factors that they seek to exploit"
    - "the group element of cybercrime types dynamics and criminal operations"

Connected through hyperedge HE-7 (induced by source paper "eviplant an efficient digital forensic challenge creation manipulation and distribution solution"):
  with members:
    - "battling the digital forensic backlog through data deduplication"
    - "bittorrent sync first impressions and digital forensic implications"

Connected through hyperedge HE-8 (induced by source paper "battling the digital forensic backlog through data deduplication"):
  with members:
    - "increasing digital investigator availability through efficient workflow management and automation"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "automated inference of past action instances in digital investigations"
    - "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)