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: "r2 d2 color inspired convolutional neural network cnn based android malware detections"
  Abstract: The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on this http URL

Connected through hyperedge HE-1 (induced by source paper "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"):
  with members:
    - "detecting and classifying android malware using static analysis along with creator information"
    - "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"
    - "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-2 (induced by source paper "using deep neural network for android malware detection"):
  with members:
    - "android malware detection using parallel machine learning classifiers"
    - "analysis of bayesian classification based approaches for android malware detection"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples attacks on machine learning based malware visualization detection methods"):
  with members:
    - "detecting and classifying android malware using static analysis along with creator information"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "adversarial deep learning for robust detection of binary encoded malware"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "efficient defenses against adversarial attacks"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "adversarial machine learning at scale"
    - "deep neural network based malware detection using two dimensional binary program features"

Connected through hyperedge HE-5 (induced by source paper "hunting the ethereum smart contract color inspired inspection of potential attacks"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"

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)