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: "detecting and classifying android malware using static analysis along with creator information"
  Abstract: Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however previous studies overlooked such information as a feature in detecting and classifying malware, and in attributing malware to creators. Guided by this insight, we propose a method to improve on the performance of Android malware detection by incorporating the creator's information as a feature and classify malicious applications into similar groups. We developed a system that implements this method in practice. Our system enables fast detection of malware by using creator information such as serial number of certificate. Additionally, it analyzes malicious be-haviors and permissions to increase detection accuracy. The system also can classify malware based on similarity scoring. Finally, we showed detection and classification performance with 98% and 90% accuracy respectively.

Connected through hyperedge HE-1 (induced by source paper "copycat practical adversarial attacks on visualization based malware detection"):
  with members:
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "microsoft malware classification challenge"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "lightweight classification of iot malware based on image recognition"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (induced by source paper "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"):
  with members:
    - "adversarial examples opportunities and challenges"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "microsoft malware classification challenge"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "r2 d2 color inspired convolutional neural network cnn based android malware detections"
    - "towards evaluating the robustness of neural networks"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "malware classification using deep learning based feature extraction and wrapper based feature selection technique"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "efficient estimation of word representations in vector space"
    - "malware classification based on call graph clustering"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples attacks on machine learning based malware visualization detection methods"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "fooling end to end speaker verification by adversarial examples"
    - "black box attacks against rnn based malware detection algorithms"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "defense against universal adversarial perturbations"

Connected through hyperedge HE-5 (induced by source paper "andro profiler detecting and classifying android malware based on behavioral profiles"):
  with members:
    - "mal netminer malware classification approach based on social network analysis of system call graph"

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)