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: "query efficient gan based black box attack against sequence based machine and deep learning classifiers"
  Abstract: In this paper we present an efficient and generic black-box attack demonstrated against API call based machine learning malware classifiers. We generate adversarial examples combining sequences (API call sequences) and other features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. Opposed to previous studies, our attack minimizes the number of target classifier queries and only requires access to the predicted label of the attacked model (without the confidence level). We evaluate the attack's effectiveness against a variety of classifiers, including recurrent neural network variants, deep neural networks, support vector machines, and gradient-boosted decision trees. We show that the attack requires fewer queries and less knowledge about the attacked model's architecture than other existing black-box attacks, making it practical for attacking cloud based models at a minimal cost. We also implement a software framework that can be used to recraft any malware binary so it will not be detected by classifiers, without access to the malware source code. Finally, we discuss the robustness of this attack to existing defense mechanisms.

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"
    - "generation evaluation of adversarial examples for malware obfuscation"
    - "examining adversarial learning against graph based iot malware detection systems"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"

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)