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: "increasing digital investigator availability through efficient workflow management and automation"
  Abstract: The growth of digital storage capacities and diversity devices has had a significant time impact on digital forensic laboratories in law enforcement. Backlogs have become commonplace and increasingly more time is spent in the acquisition and preparation steps of an investigation as opposed to detailed evidence analysis and reporting. There is generally little room for increasing digital investigation capacity in law enforcement digital forensic units and the allocated budgets for these units are often decreasing. In the context of developing an efficient investigation process, one of the key challenges amounts to how to achieve more with less. This paper proposes a workflow management automation framework for handling common digital forensic tools. The objective is to streamline the digital investigation workflow - enabling more efficient use of limited hardware and software. The proposed automation framework reduces the time digital forensic experts waste conducting time-consuming, though necessary, tasks. The evidence processing time is decreased through server-side automation resulting in 24/7 evidence preparation. The proposed framework increases efficiency of use of forensic software and hardware, reduces the infrastructure costs and license fees, and simplifies the preparation steps for the digital investigator. The proposed approach is evaluated in a real-world scenario to evaluate its robustness and highlight its benefits.

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"
    - "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"
    - "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"
    - "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"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "battling the digital forensic backlog through data deduplication"):
  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"
    - "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)