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: "malware evasion attack and defense"
  Abstract: Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which can be modified slightly to intentionally cause an ML classifier to misclassify it. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conducted performance evaluations in a real-world setting. We propose a framework for deploying grey-box and black-box attacks to malware detection systems. We compared the defense approaches in mitigating the attacks.

Connected through hyperedge HE-1 (induced by source paper "can machine learning model with static features be fooled an adversarial machine learning approach"):
  with members:
    - "similarity based android malware detection using hamming distance of static binary features"
    - "physical adversarial examples for object detectors"
    - "audio adversarial examples targeted attacks on speech to text"
    - "fooling end to end speaker verification by adversarial examples"
    - "on the statistical detection of adversarial examples"
    - "adversarial examples in the physical world"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "the limitations of deep learning in adversarial settings"

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)