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: "semantic adversarial examples"
  Abstract: Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error. Such images, however, contain artificial perturbations that make them somewhat distinguishable from natural images. This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations. #R##N#In this paper, we introduce a new class of adversarial examples, namely "Semantic Adversarial Examples," as images that are arbitrarily perturbed to fool the model, but in such a way that the modified image semantically represents the same object as the original image. We formulate the problem of generating such images as a constrained optimization problem and develop an adversarial transformation based on the shape bias property of human cognitive system. In our method, we generate adversarial images by first converting the RGB image into the HSV (Hue, Saturation and Value) color space and then randomly shifting the Hue and Saturation components, while keeping the Value component the same. Our experimental results on CIFAR10 dataset show that the accuracy of VGG16 network on adversarial color-shifted images is 5.7%.

Connected through hyperedge HE-1 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "functional adversarial attacks"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "generating realistic unrestricted adversarial inputs using dual objective gan training"

Connected through hyperedge HE-2 (induced by source paper "semantic adversarial perturbations using learnt representations"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "algorithms for verifying deep neural networks"
    - "generating semantic adversarial examples with differentiable rendering"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "adaptive generation of unrestricted adversarial inputs"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial camouflage hiding physical world attacks with natural styles"):
  with members:
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "black box adversarial attacks on video recognition models"
    - "adversarial t shirt evading person detectors in a physical world"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-4 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "functional adversarial attacks"
    - "a general framework for adversarial examples with objectives"
    - "panoptic feature pyramid networks"
    - "spatially transformed adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "type i attack for generative models"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "adversarial attacks on variational autoencoders"
    - "arcface additive angular margin loss for deep face recognition"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "adversarial attack type i cheat classifiers by significant changes"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-6 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "online normalization for training neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "when does label smoothing help"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "stand alone self attention in vision models"
    - "are perceptually aligned gradients a general property of robust classifiers"

Connected through hyperedge HE-7 (induced by source paper "daedalus breaking non maximum suppression in object detection via adversarial examples"):
  with members:
    - "model reuse attacks on deep learning systems"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "deep learning for generic object detection a survey"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "yolov3 an incremental improvement"
    - "multi task learning as multi objective optimization"
    - "traits transferability of adversarial examples against instance segmentation object detection"

Connected through hyperedge HE-8 (induced by source paper "natural adversarial examples"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "towards neural networks that provably know when they don t know"
    - "verified uncertainty calibration"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "detecting semantic anomalies"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

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)