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: "big but imperceptible adversarial perturbations via semantic manipulation"
  Abstract: Machine learning, especially deep learning, is widely applied to a range of applications including computer vision, robotics and natural language processing. However, it has been shown that machine learning models are vulnerable to adversarial examples, carefully crafted samples that deceive learning models. In-depth studies on adversarial examples can help better understand potential vulnerabilities and therefore improve model robustness. Recent works have introduced various methods which generate adversarial examples. However, all require the perturbation to be of small magnitude ($\mathcal{L}_p$ norm) for them to be imperceptible to humans, which is hard to deploy in practice. In this paper we propose two novel methods, tAdv and cAdv, which leverage texture transfer and colorization to generate natural perturbation with a large $\mathcal{L}_p$ norm. We conduct extensive experiments to show that the proposed methods are general enough to attack both image classification and image captioning tasks on ImageNet and MSCOCO dataset. In addition, we conduct comprehensive user studies under various conditions to show that our generated adversarial examples are imperceptible to humans even when the perturbations are large. We also evaluate the transferability and robustness of the proposed attacks against several state-of-the-art defenses.

Connected through hyperedge HE-1 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"

Connected through hyperedge HE-2 (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"

Connected through hyperedge HE-3 (induced by source paper "structure matters towards generating transferable adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "adversarial training for free"
    - "semantic adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "exploring adversarial examples in malware detection"

Connected through hyperedge HE-4 (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"
    - "a general framework for adversarial examples with objectives"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "functional adversarial attacks"):
  with members:
    - "transfer of adversarial robustness between perturbation types"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "constructing unrestricted adversarial examples with generative models"
    - "exploring the landscape of spatial robustness"

Connected through hyperedge HE-6 (induced by source paper "playing it safe adversarial robustness with an abstain option"):
  with members:
    - "controlling neural level sets"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "functional adversarial attacks"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "spatially transformed adversarial examples"
    - "are adversarial examples inevitable"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"

Connected through hyperedge HE-7 (induced by source paper "colorfool semantic adversarial colorization"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "semantic adversarial examples"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "sparsefool a few pixels make a big difference"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

Connected through hyperedge HE-8 (induced by source paper "towards verifying robustness of neural networks against semantic perturbations"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "towards analyzing semantic robustness of deep neural networks"
    - "scaling provable adversarial defenses"
    - "semantic adversarial examples"
    - "a dual approach to scalable verification of deep networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "efficient formal safety analysis of 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)