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 the classifiers to detect android malicious apps"):
  Abstract: Android devices are growing exponentially and are connected through the Internet accessing billion of online Websites. The popularity of these devices encourages malware developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although for the detection of malicious apps different approaches are discussed. However, proposed approaches are not sufficed to detect the advanced malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcode occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy (~79.27%) is best among the investigated classifiers. However, true positives rate, i.e. malware detection rate is highest (~99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.

Includes members:
  Member "an effective approach for classification of advanced malware with high 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 "n opcode analysis for android malware classification and categorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dl droid deep learning based android malware detection using real devices")
  Member "evolution and detection of polymorphic and metamorphic malwares a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the detection of android malicious apps")

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)