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: "adversarial deep learning for robust detection of binary encoded malware"
  Abstract: Malware is constantly adapting in order to avoid detection. Model based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to adversarial examples of images have been developed using saddle-point optimization formulations. We are inspired by them to develop similar methods for the discrete, e.g. binary, domain which characterizes the features of malware. A specific extra challenge of malware is that the adversarial examples must be generated in a way that preserves their malicious functionality. We introduce methods capable of generating functionally preserved adversarial malware examples in the binary domain. Using the saddle-point formulation, we incorporate the adversarial examples into the training of models that are robust to them. We evaluate the effectiveness of the methods and others in the literature on a set of Portable Execution~(PE) files. Comparison prompts our introduction of an online measure computed during training to assess general expectation of robustness.

Connected through hyperedge HE-1 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "black box attacks against rnn based malware detection algorithms"
    - "attack and defense of dynamic analysis based adversarial neural malware classification models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "hardening malware detection systems against cyber maneuvers an adversarial machine learning approach"
    - "improved training of wasserstein gans"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "there are no bit parts for sign bits in black box attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "exploring the space of black box attacks on deep neural networks"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-3 (induced by source paper "ten ai stepping stones for cybersecurity"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "provable limitations of deep learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "a first look at deep learning apps on smartphones"
    - "prada protecting against dnn model stealing attacks"
    - "logan membership inference attacks against generative models"
    - "malicious url detection using machine learning a survey"

Connected through hyperedge HE-4 (induced by source paper "towards a robust classifier an mdl based method for generating adversarial examples"):
  with members:
    - "ember an open dataset for training static pe malware machine learning models"
    - "exploring the space of black box attacks on deep neural networks"
    - "generating adversarial examples with adversarial networks"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "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"
    - "generating natural adversarial examples"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-5 (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"
    - "detecting and classifying android malware using static analysis along with creator information"
    - "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-6 (induced by source paper "a survey of moving target defenses for network security"):
  with members:
    - "toward a theory of cyber attacks"
    - "adaptive mtd security using markov game modeling"
    - "trufl distributed trust management framework in sdn"
    - "markov game modeling of moving target defense for strategic detection of threats in cloud networks"
    - "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense"
    - "moving target defense for web applications using bayesian stackelberg games"
    - "software defined networking a comprehensive survey"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "an efficient detection of malware by naive bayes classifier using gpgpu"):
  with members:
    - "group wise classification approach to improve android malicious apps detection accuracy"
    - "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"

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)