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: "generation evaluation of adversarial examples for malware obfuscation"
  Abstract: There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to adversarial examples allows malicious actors to evade classifiers. Adversarial examples are usually generated by adding small perturbations to the input that are unrecognizable to humans, but the same approach is not effective with malware. In general, these perturbations cause changes in the byte sequences that change the initial functionality or result in un-executable binaries. Adversaries typically employ program obfuscation methods to complicate and conceal the true intent of malware without affecting the program's logic, but these techniques are ad-hoc and are not guaranteed to evade strong malware classifiers. Our approach uses a novel dynamic programming obfuscation algorithm and the adversarial perturbations for a classifier to modify an input malware to fool the classifier while ensuring that it is executable. This is the first work, to the best of our knowledge, that creates executable adversarial malware examples using obfuscation, thus not limiting perturbations to non-executable sections of a binary. Our method achieves a high misclassification rate, up to 100% and ~98% in white-box and black-box settings respectively, and demonstrates transferability between different models. We further evaluate the effectiveness of the proposed method by reporting insignificant change in the evasion rate of our adversarial examples against popular defense strategies.

Connected through hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers"):
  with members:
    - "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "on defending against label flipping attacks on malware detection systems"
    - "adversarial samples on android malware detection systems for iot systems"
    - "copycat practical adversarial attacks on visualization based malware detection"
    - "examining adversarial learning against graph based iot malware detection systems"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "easy to fool testing the anti evasion capabilities of pdf malware scanners"

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)