human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "analysis of bayesian classification based approaches for android malware detection"
  Abstract: Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Connected through hyperedge HE-1 (induced by source paper "using deep neural network for android malware detection"):
  with members:
    - "r2 d2 color inspired convolutional neural network cnn based android malware detections"
    - "android malware detection using parallel machine learning classifiers"

Connected through hyperedge HE-2 (induced by source paper "android malware characterization using metadata and machine learning techniques"):
  with members:
    - "high accuracy android malware detection using ensemble learning"
    - "droidanalytics a signature based analytic system to collect extract analyze and associate android malware"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "dynalog an automated dynamic analysis framework for characterizing android applications"):
  with members:
    - "android malware detection an eigenspace analysis approach"
    - "enter sandbox android sandbox comparison"
    - "a new android malware detection approach using bayesian classification"
    - "ananas a framework for analyzing android applications"

Connected through hyperedge HE-4 (induced by source paper "n opcode analysis for android malware classification and categorization"):
  with members:
    - "android malware detection an eigenspace analysis approach"
    - "high accuracy android malware detection using ensemble learning"

Connected through hyperedge HE-5 (induced by source paper "android malware detection an eigenspace analysis approach"):
  with members:
    - "android malware detection using parallel machine learning classifiers"
    - "a new android malware detection approach using bayesian classification"

Connected through hyperedge HE-6 (induced by source paper "high accuracy android malware detection using ensemble learning"):
  with members:
    - "a new android malware detection approach using bayesian classification"

Connected through hyperedge HE-7 (induced by source paper "android malware detection using parallel machine learning classifiers"):
  with members:
    - "a new android malware detection approach using bayesian classification"

Question: Please tell me which class the center node 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)