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: "spatially transformed adversarial examples"
  Abstract: Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the $\mathcal{L}_p$ distance for penalizing perturbations. Researchers have explored different defense methods to defend against such adversarial attacks. While the effectiveness of $\mathcal{L}_p$ distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works. Perturbations generated through spatial transformation could result in large $\mathcal{L}_p$ distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses. We visualize the spatial transformation based perturbation for different examples and show that our technique can produce realistic adversarial examples with smooth image deformation. Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted.

Connected through hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "hopskipjumpattack a query efficient decision based attack"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"

Connected through hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "deep double descent where bigger models and more data hurt"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"

Connected through hyperedge HE-3 (induced by source paper "varmixup exploiting the latent space for robust training and inference"):
  with members:
    - "adversarial domain adaptation with domain mixup"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "harnessing the vulnerability of latent layers in adversarially trained models"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-4 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems"):
  with members:
    - "taxonomy and challenges of out of band signal injection attacks and defenses"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "advhat real world adversarial attack on arcface face id system"
    - "stochastic activation pruning for robust adversarial defense"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "data augmentation with symbolic to real image translation gans for traffic sign recognition"
    - "real to virtual domain unification for end to end autonomous driving"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "multimodal unsupervised image to image translation"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces"):
  with members:
    - "fast geometrically perturbed adversarial faces"
    - "style aggregated network for facial landmark detection"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "more is less a more complicated network with less inference complexity"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "provable robust learning based on transformation specific smoothing"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "heat and blur an effective and fast defense against adversarial examples"):
  with members:
    - "concise explanations of neural networks using adversarial training"
    - "adversarial examples are a natural consequence of test error in noise"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "saliency methods for explaining adversarial attacks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "interpretable deep learning under fire"
    - "concise and stable explanations using adversarial training"
    - "feature squeezing detecting adversarial examples in deep neural networks"

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)