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 "dl droid deep learning based android malware detection using real devices"):
  Abstract: Abstract   The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.

Includes members:
  Member "improving dynamic analysis of android apps using hybrid test input generation":
    also connected through other hyperedges:
      (none)
  Member "android malware detection using deep learning on api method sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "emulator vs real phone android malware detection using machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving dynamic analysis of android apps using hybrid test input generation")
  Member "n gram opcode analysis for android malware detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards safer smart contracts a sequence learning approach to detecting security threats")
  Member "n opcode analysis for android malware classification and categorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new era of deeplearning based malware intrusion detection the malware detection and prediction based on deep learning")
  Member "dynalog an automated dynamic analysis framework for characterizing android applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "portable data driven malware detection using language processing and machine learning techniques on behavioral analysis reports")
  Member "android malware detection an eigenspace analysis approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ml fv heartsuit a survey on the application of machine learning to formal verification")
  Member "automated test input generation for android are we there yet":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "to the attention of mobile software developers guess what test your app")

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)