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: "transfer of adversarial robustness between perturbation types"
  Abstract: We study the transfer of adversarial robustness of deep neural networks between different perturbation types. While most work on adversarial examples has focused on $L_\infty$ and $L_2$-bounded perturbations, these do not capture all types of perturbations available to an adversary. The present work evaluates 32 attacks of 5 different types against models adversarially trained on a 100-class subset of ImageNet. Our empirical results suggest that evaluating on a wide range of perturbation sizes is necessary to understand whether adversarial robustness transfers between perturbation types. We further demonstrate that robustness against one perturbation type may not always imply and may sometimes hurt robustness against other perturbation types. In light of these results, we recommend evaluation of adversarial defenses take place on a diverse range of perturbation types and sizes.

Connected through hyperedge HE-1 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"

Connected through hyperedge HE-2 (induced by source paper "provable robustness against all adversarial l_p perturbations for p geq 1"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial patch"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-3 (induced by source paper "adversarial training and robustness for multiple perturbations"):
  with members:
    - "adversarial perceptual ad blocking meets adversarial machine learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "on the geometry of adversarial examples"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial spheres"

Connected through hyperedge HE-4 (induced by source paper "adversarial robustness against the union of multiple perturbation models"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "logit pairing methods can fool gradient based attacks"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "adversarial logit pairing"
    - "towards the first adversarially robust neural network model on mnist"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "functional adversarial attacks"):
  with members:
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "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 "universal adversarial perturbations to understand robustness of texture vs shape biased training"):
  with members:
    - "improving the robustness of imagenet classifiers using elements of human visual cognition"
    - "adversarial training and robustness for multiple perturbations"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "sensitivity of deep convolutional networks to gabor noise"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "universal adversarial training"
    - "generalizable data free objective for crafting universal adversarial perturbations"

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)