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 attacks hidden in plain sight"
  Abstract: Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making any desired incorrect classification, potentially with very high certainty. Several defensive approaches increase robustness against adversarial attacks, demanding attacks of greater magnitude, which lead to visible artifacts. By considering human visual perception, we compose a technique that allows to hide such adversarial attacks in regions of high complexity, such that they are imperceptible even to an astute observer. We carry out a user study on classifying adversarially modified images to validate the perceptual quality of our approach and find significant evidence for its concealment with regards to human visual perception.

Connected through hyperedge HE-1 (induced by source paper "adversarial robustness curves"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial robustness may be at odds with simplicity"
    - "measuring neural net robustness with constraints"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "practical black box attacks against deep learning systems using adversarial examples"
    - "adversarial machine learning at scale"
    - "learning with a strong adversary"

Connected through hyperedge HE-2 (induced by source paper "recovering localized adversarial attacks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "why should i trust you explaining the predictions of any classifier"
    - "adversarial machine learning at scale"
    - "deep learning in neural networks"
    - "rethinking the inception architecture for computer vision"

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.LG(Machine Learning)