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 "deep learning at the shallow end malware classification for non domain experts"):
  Abstract: Abstract   Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.

Includes members:
  Member "microsoft malware classification challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpreting machine learning malware detectors which leverage n gram analysis")
  Member "network intell enabling the non expert analysis of large volumes of intercepted network traffic":
    also connected through other hyperedges:
      (none)
  Member "recent trends in deep learning based natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "increasing digital investigator availability through efficient workflow management and automation":
    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 "integration of ether unpacker into ragpicker for plugin based malware analysis and identification":
    also connected through other hyperedges:
      (none)
  Member "battling the digital forensic backlog through data deduplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts")
  Member "tiered forensic methodology model for digital field triage by non digital evidence specialists":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts")
  Member "deepsign deep learning for automatic malware signature generation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems")

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.CR(Cryptography and Security)