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: "distributionally adversarial attack"
  Abstract: Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution $p(\mathbf{x})$, typically in the form of risk maximization/minimization, e.g., $\max/\min\mathbb{E}_{p(\mathbf(x))}\mathcal{L}(\mathbf{x})$ with $p(\mathbf{x})$ some unknown data distribution and $\mathcal{L}(\cdot)$ a loss function. However, since PGD generates attack samples independently for each data sample based on $\mathcal{L}(\cdot)$, the procedure does not necessarily lead to good generalization in terms of risk optimization. In this paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal {\em adversarial-data distribution}, a perturbed distribution that satisfies the $L_\infty$ constraint but deviates from the original data distribution to increase the generalization risk maximally. Algorithmically, DAA performs optimization on the space of potential data distributions, which introduces direct dependency between all data points when generating adversarial samples. DAA is evaluated by attacking state-of-the-art defense models, including the adversarially-trained models provided by {\em MIT MadryLab}. Notably, DAA ranks {\em the first place} on MadryLab's white-box leaderboards, reducing the accuracy of their secret MNIST model to $88.79\%$ (with $l_\infty$ perturbations of $\epsilon = 0.3$) and the accuracy of their secret CIFAR model to $44.71\%$ (with $l_\infty$ perturbations of $\epsilon = 8.0$). Code for the experiments is released on \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "output diversified initialization for adversarial attacks"):
  with members:
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "theoretically principled trade off between robustness and accuracy"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"

Connected through hyperedge HE-2 (induced by source paper "towards sharper first order adversary with quantized gradients"):
  with members:
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "mixtrain scalable training of formally robust neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on machine learning models"
    - "countering adversarial images using input transformations"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "mitigating adversarial effects through randomization"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-3 (induced by source paper "utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks"):
  with members:
    - "increasing the robustness of dnns against image corruptions by playing the game of noise"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "towards stable and efficient training of verifiably robust neural networks"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "certified adversarial robustness via randomized smoothing"
    - "a provable defense for deep residual networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "verification of non linear specifications for neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-5 (induced by source paper "square attack a query efficient black box adversarial attack via random search"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "sparse and imperceivable adversarial attacks"
    - "black box adversarial attacks with bayesian optimization"
    - "certified adversarial robustness via randomized smoothing"
    - "hopskipjumpattack a query efficient decision based attack"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "there are no bit parts for sign bits in black box attacks"

Connected through hyperedge HE-6 (induced by source paper "minimally distorted adversarial examples with a fast adaptive boundary attack"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"
    - "adversarial patch"
    - "logit pairing methods can fool gradient based attacks"
    - "sparsefool a few pixels make a big difference"

Connected through hyperedge HE-7 (induced by source paper "there are no bit parts for sign bits in black box attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "exploring the space of black box attacks on deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "an alternative surrogate loss for pgd based adversarial testing"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "enhancing gradient based attacks with symbolic intervals"
    - "theoretically principled trade off between robustness and accuracy"
    - "on evaluating adversarial robustness"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"

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)