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: "challenges with automation in digital forensic investigations"
  Abstract: The use of automation in digital forensic investigations is not only a technological issue, but also has political and social implications. This work discusses some challenges with the implementation and acceptance of automation in digital forensic investigation, and possible implications for current digital forensic investigators. Current attitudes towards the use of automation in digital forensic investigations are examined, as well as the issue of digital investigators knowledge acquisition and retention. The argument is made for a well planned, careful use of automation going forward that allows for a more efficient and effective use of automation in digital forensic investigations while at the same time attempting to improve the overall quality of expert investigators. Targeting and carefully controlling automated solutions for beginning investigators may improve the speed and quality of investigations while at the same time letting expert digital investigators spend more time utilizing expert level knowledge required in manual phases of investigations. By considering how automated solutions are being implemented into digital investigations, investigation unit managers can increase the efficiency of their unit while at the same time maximizing their return on investment for expert level digital investigator training.

Connected through hyperedge HE-1 (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"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "deepsign deep learning for automatic malware signature generation and classification"

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

Connected through hyperedge HE-3 (induced by source paper "an assessment model for cybercrime investigation capacity"):
  with members:
    - "measuring digital crime investigation capacity to guide international crime prevention strategies"

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)