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 "towards the leveraging of data deduplication to break the disk acquisition speed limit"):
  Abstract: Digital forensic evidence acquisition speed is traditionally limited by two main factors: the read speed of the storage device being investigated, i.e., the read speed of the disk, memory, remote storage, mobile device, etc.), and the write speed of the system used for storing the acquired data. Digital forensic investigators can somewhat mitigate the latter issue through the use of high-speed storage options, such as networked RAID storage, in the controlled environment of the forensic laboratory. However, traditionally, little can be done to improve the acquisition speed past its physical read speed from the target device itself. The protracted time taken for data acquisition wastes digital forensic experts' time, contributes to digital forensic investigation backlogs worldwide, and delays pertinent information from potentially influencing the direction of an investigation. In a remote acquisition scenario, a third contributing factor can also become a detriment to the overall acquisition time -- typically the Internet upload speed of the acquisition system. This paper explores an alternative to the traditional evidence acquisition model through the leveraging of a forensic data deduplication system. The advantages that a deduplicated approach can provide over the current digital forensic evidence acquisition process are outlined and some preliminary results of a prototype implementation are discussed.

Includes members:
  Member "battling the digital forensic backlog through data deduplication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts")
  Member "current challenges and future research areas for digital forensic investigation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts")
  Member "network investigation methodology for bittorrent sync":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "forensic investigation of p2p cloud storage bittorrent sync as a case study")
  Member "challenges with automation in digital forensic investigations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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.CY(Computers and Society)