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: "a general framework for adversarial examples with objectives"
  Abstract: Images perturbed subtly to be misclassified by neural networks, called adversarial examples, have emerged as a technically deep challenge and an important concern for several application domains. Most research on adversarial examples takes as its only constraint that the perturbed images are similar to the originals. However, real-world application of these ideas often requires the examples to satisfy additional objectives, which are typically enforced through custom modifications of the perturbation process. In this article, we propose adversarial generative nets (AGNs), a general methodology to train a generator neural network to emit adversarial examples satisfying desired objectives. We demonstrate the ability of AGNs to accommodate a wide range of objectives, including imprecise ones difficult to model, in two application domains. In particular, we demonstrate physical adversarial examples—eyeglass frames designed to fool face recognition—with better robustness, inconspicuousness, and scalability than previous approaches, as well as a new attack to fool a handwritten-digit classifier.

Connected through hyperedge HE-1 (induced by source paper "advjnd generating adversarial examples with just noticeable difference"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "perceptual evaluation of adversarial attacks for cnn based image classification"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "black box attacks against rnn based malware detection algorithms"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "densely connected convolutional networks"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (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"
    - "panoptic feature pyramid networks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "attacking optical flow"
    - "on the in fidelity and sensitivity for explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visualbackprop efficient visualization of cnns"

Connected through hyperedge HE-4 (induced by source paper "advhat real world adversarial attack on arcface face id system"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "adversarial patch"
    - "cosface large margin cosine loss for deep face recognition"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-5 (induced by source paper "advfaces adversarial face synthesis"):
  with members:
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "spatially transformed adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-6 (induced by source paper "design and interpretation of universal adversarial patches in face detection"):
  with members:
    - "towards adversarially robust object detection"
    - "theoretically principled trade off between robustness and accuracy"
    - "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "on physical adversarial patches for object detection"
    - "advhat real world adversarial attack on arcface face id system"
    - "dsfd dual shot face detector"
    - "adversarial patch"

Connected through hyperedge HE-7 (induced by source paper "towards large yet imperceptible adversarial image perturbations with perceptual color distance"):
  with members:
    - "perceptual quality preserving black box attack against deep learning image classifiers"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "smooth adversarial examples"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "adaptive generation of unrestricted adversarial inputs"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "global adversarial attacks for assessing deep learning robustness"
    - "how good is my gan"
    - "rmdl random multimodel deep learning for classification"
    - "towards the first adversarially robust neural network model on mnist"
    - "mixtrain scalable training of formally robust 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.CV(Computer Vision and Pattern Recognition)