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 "android malware detection based on factorization machine"):
  Abstract: With the increasing popularity of Android smart phones in recent years, the number of Android malware is growing rapidly. Due to its great threat and damage to mobile phone users, Android malware detection has become increasingly important in cyber security. Traditional methods like signature-based ones cannot protect users from the ever increasing sophistication and rapid behavior changes of new types of Android malware. Therefore, lots of recent efforts have been made to use machine learning to characterize and discover the malicious behavior patterns of mobile apps for malware detection. In this paper, we propose a novel and highly reliable machine learning algorithm for Android Malware detection based on the use of Factorization Machine and the extensive study of Android app features. We first extract 77 types of features that are highly relevant to malware detection from the manifest file and source code of each mobile app, including Application Programming Interface (API) calls, permissions, etc. We have observed that the numerical feature representation of an app usually forms a long and highly sparse vector, and that the interactions among different features are critical to revealing some malicious behavior patterns. Based on these observations, we propose to use factorization machine, which fit the problem the best, as a supervised classifier for malware detection. According to extensive performance evaluation, our proposed method achieved a test result of 99.01% detection rate with 0.09% false positive rate on the DREBIN dataset and 99.2% detection rate with only 0.93% false positive rate on the AMD dataset, significantly outperformed a number of state-of-the-art machine-learning-based Android malware detection methods as well as commercial antivirus engines.

Includes members:
  Member "leaksemantic identifying abnormal sensitive network transmissions in mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "android malware detection using large scale network representation learning")
  Member "mamadroid detecting android malware by building markov chains of behavioral models extended version":
    also connected through other hyperedges:
      (none)
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "sound and precise malware analysis for android via pushdown reachability and entry point saturation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dexteroid detecting malicious behaviors in android apps using reverse engineered life cycle models")

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)