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: "battling the digital forensic backlog through data deduplication"
  Abstract: In everyday life. Technological advancement can be found in many facets of life, including personal computers, mobile devices, wearables, cloud services, video gaming, web-powered messaging, social media, Internet-connected devices, etc. This technological influence has resulted in these technologies being employed by criminals to conduct a range of crimes -- both online and offline. Both the number of cases requiring digital forensic analysis and the sheer volume of information to be processed in each case has increased rapidly in recent years. As a result, the requirement for digital forensic investigation has ballooned, and law enforcement agencies throughout the world are scrambling to address this demand. While more and more members of law enforcement are being trained to perform the required investigations, the supply is not keeping up with the demand. Current digital forensic techniques are arduously time-consuming and require a significant amount of man power to execute. This paper discusses a novel solution to combat the digital forensic backlog. This solution leverages a deduplication-based paradigm to eliminate the reacquisition, redundant storage, and reanalysis of previously processed data.

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"
    - "current challenges and future research areas for digital forensic investigation"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"

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"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "privileged data within digital evidence"):
  with members:
    - "privacy preserving internet browsers forensic analysis of browzar"
    - "a bespoke forensics gis tool"
    - "forensics acquisition and analysis of instant messaging and voip applications"

Connected through hyperedge HE-4 (induced by source paper "eviplant an efficient digital forensic challenge creation manipulation and distribution solution"):
  with members:
    - "current challenges and future research areas for digital forensic investigation"
    - "bittorrent sync first impressions and digital forensic implications"

Connected through hyperedge HE-5 (induced by source paper "hierarchical bloom filter trees for approximate matching"):
  with members:
    - "automated inference of past action instances in digital investigations"

Connected through hyperedge HE-6 (induced by source paper "network intell enabling the non expert analysis of large volumes of intercepted network traffic"):
  with members:
    - "towards the forensic identification and investigation of cloud hosted servers through non invasive wiretaps"

Connected through hyperedge HE-7 (induced by source paper "towards the leveraging of data deduplication to break the disk acquisition speed limit"):
  with members:
    - "current challenges and future research areas for digital forensic investigation"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "network investigation methodology for bittorrent sync"
    - "challenges with automation in digital forensic investigations"

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.CY(Computers and Society)