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: "vectordefense vectorization as a defense to adversarial examples"
  Abstract: Training deep neural networks on images represented as grids of pixels has brought to light an interesting phenomenon known as adversarial examples. Inspired by how humans reconstruct abstract concepts, we attempt to codify the input bitmap image into a set of compact, interpretable elements to avoid being fooled by the adversarial structures. We take the first step in this direction by experimenting with image vectorization as an input transformation step to map the adversarial examples back into the natural manifold of MNIST handwritten digits. We compare our method vs. state-of-the-art input transformations and further discuss the trade-offs between a hand-designed and a learned transformation defense.

Connected through hyperedge HE-1 (induced by source paper "deep vectorization of technical drawings"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "stroke based sketched symbol reconstruction and segmentation"
    - "vectorization of line drawings via polyvector fields"
    - "a complete hand drawn sketch vectorization framework"
    - "abc a big cad model dataset for geometric deep learning"
    - "attention is all you need"
    - "mastering sketching adversarial augmentation for structured prediction"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-2 (induced by source paper "understanding neural networks via feature visualization a survey"):
  with members:
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "decoding brain representations by multimodal learning of neural activity and visual features"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "neural photo editing with introspective adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "towards the first adversarially robust neural network model on mnist"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "deflecting adversarial attacks with pixel deflection"
    - "black box adversarial attacks with limited queries and information"
    - "distributionally adversarial attack"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "funn flexible unsupervised neural network"):
  with members:
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "adversarial defense based on structure to signal autoencoders"
    - "magnet a two pronged defense against adversarial examples"
    - "ape gan adversarial perturbation elimination with gan"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "simple black box adversarial perturbations for deep networks"
    - "adversarial examples attacks and defenses for deep learning"

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.CV(Computer Vision and Pattern Recognition)