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: "semanticadv generating adversarial examples via attribute conditional image editing"
  Abstract: Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples which are manipulated instances targeting to mislead DNNs to make incorrect predictions. Currently, most such adversarial examples try to guarantee "subtle perturbation" by limiting the $L_p$ norm of the perturbation. In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples". #R##N#In particular, we propose an algorithm \emph{SemanticAdv} which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets. We conduct extensive experiments to show that the semantic based adversarial examples can not only fool different learning tasks such as face verification and landmark detection, but also achieve high targeted attack success rate against \emph{real-world black-box} services such as Azure face verification service based on transferability. #R##N#To further demonstrate the applicability of \emph{SemanticAdv} beyond face recognition domain, we also generate semantic perturbations on street-view images. Such adversarial examples with controlled semantic manipulation can shed light on further understanding about vulnerabilities of DNNs as well as potential defensive approaches.

Connected through hyperedge HE-1 (induced by source paper "semantic adversarial perturbations using learnt representations"):
  with members:
    - "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"
    - "semantic adversarial examples"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images"):
  with members:
    - "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"
    - "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 "achieving robustness in the wild via adversarial mixing with disentangled representations"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "natural adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "do imagenet classifiers generalize to imagenet"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"

Connected through hyperedge HE-4 (induced by source paper "structure matters towards generating transferable adversarial images"):
  with members:
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "adversarial training for free"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "semantic adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "exploring adversarial examples in malware detection"

Connected through hyperedge HE-5 (induced by source paper "generating semantic adversarial examples with differentiable rendering"):
  with members:
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "counterexample guided data augmentation"
    - "3d aware scene manipulation via inverse graphics"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-6 (induced by source paper "detection of face recognition adversarial attacks"):
  with members:
    - "adversarial image translation unrestricted adversarial examples in face recognition systems"
    - "on the robustness of deep k nearest neighbors"
    - "on adversarial patches real world attack on arcface 100 face recognition system"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "deep face recognition a survey"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "picture it in your mind generating high level visual representations from textual descriptions"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-7 (induced by source paper "characterizing attacks on deep reinforcement learning"):
  with members:
    - "risk averse robust adversarial reinforcement learning"
    - "meshadv adversarial meshes for visual recognition"
    - "how you act tells a lot privacy leakage attack on deep reinforcement learning"
    - "spatially transformed adversarial examples"
    - "assessing generalization in deep reinforcement learning"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "exploring the space of black box attacks on deep neural networks"
    - "generating adversarial examples with adversarial 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.LG(Machine Learning)