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: "group wise classification approach to improve android malicious apps detection accuracy"
  Abstract: In the fast-growing smart devices, Android is the most popular OS, and due to its attractive features, mobility, ease of use, these devices hold sensitive information such as personal data, browsing history, shopping history, financial details, etc. Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the malware. These malware are continuously growing and are also used for military espionage, disrupting the industry, power grids, etc. To detect these malware, traditional signature matching techniques are widely used. However, such strategies are not capable to detect the advanced Android malicious apps because malware developer uses several obfuscation techniques. Hence, researchers are continuously addressing the security issues in the Android based smart devices. Therefore, in this paper using Drebin benchmark malware dataset we experimentally demonstrate how to improve the detection accuracy by analyzing the apps after grouping the collected data based on the permissions and achieved 97.15% overall average accuracy. Our results outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%, 2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that among the groups, Microphone group detection accuracy is least while Calendar group apps are detected with the highest accuracy, and with the highest accuracy, and for the best performance, one shall take 80-100 features.

Connected through hyperedge HE-1 (induced by source paper "a survey on the detection of android malicious apps"):
  with members:
    - "an investigation of the classifiers to detect android malicious apps"
    - "an effective approach for classification of advanced malware with high accuracy"
    - "adaptive and scalable android malware detection through online learning"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "n opcode analysis for android malware classification and categorization"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"
    - "droidanalytics a signature based analytic system to collect extract analyze and associate android malware"

Connected through hyperedge HE-2 (induced by source paper "an efficient detection of malware by naive bayes classifier using gpgpu"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "microsoft malware classification challenge"
    - "an effective approach for classification of advanced malware with high accuracy"
    - "acceleration of statistical detection of zero day malware in the memory dump using cuda enabled gpu hardware"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-3 (induced by source paper "android malicious application classification using clustering"):
  with members:
    - "an investigation of the classifiers to detect android malicious apps"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "an investigation of a deep learning based malware detection system"):
  with members:
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "a multi task learning model for malware classification with useful file access pattern from api call sequence"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "deep neural network based malware detection using two dimensional binary program features"

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)