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 "an investigation of a deep learning based malware detection system"):
  Abstract: We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and an False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.

Includes members:
  Member "group wise classification approach to improve android malicious apps detection accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the detection of android malicious apps")
  Member "improving the detection accuracy of unknown malware by partitioning the executables in groups":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the detection of android malicious apps")
  Member "a multi task learning model for malware classification with useful file access pattern from api call sequence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "comparison of deep learning and the classical machine learning algorithm for the malware detection")
  Member "deepsign deep learning for automatic malware signature generation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems")
  Member "novel feature extraction selection and fusion for effective malware family classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection")
  Member "deep neural network based malware detection using two dimensional binary program features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")

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)