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: "an alternative surrogate loss for pgd based adversarial testing"
  Abstract: Adversarial testing methods based on Projected Gradient Descent (PGD) are widely used for searching norm-bounded perturbations that cause the inputs of neural networks to be misclassified. This paper takes a deeper look at these methods and explains the effect of different hyperparameters (i.e., optimizer, step size and surrogate loss). We introduce the concept of MultiTargeted testing, which makes clever use of alternative surrogate losses, and explain when and how MultiTargeted is guaranteed to find optimal perturbations. Finally, we demonstrate that MultiTargeted outperforms more sophisticated methods and often requires less iterative steps than other variants of PGD found in the literature. Notably, MultiTargeted ranks first on MadryLab's white-box MNIST and CIFAR-10 leaderboards, reducing the accuracy of their MNIST model to 88.36% (with $\ell_\infty$ perturbations of $\epsilon = 0.3$) and the accuracy of their CIFAR-10 model to 44.03% (at $\epsilon = 8/255$). MultiTargeted also ranks first on the TRADES leaderboard reducing the accuracy of their CIFAR-10 model to 53.07% (with $\ell_\infty$ perturbations of $\epsilon = 0.031$).

Connected through hyperedge HE-1 (induced by source paper "on adaptive attacks to adversarial example defenses"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial example detection and classification with asymmetrical adversarial training"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "resisting adversarial attacks by k winners take all"
    - "rethinking softmax cross entropy loss for adversarial robustness"
    - "the odds are odd a statistical test for detecting adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "controlling neural level sets"
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "jacobian adversarially regularized networks for robustness"
    - "improving adversarial robustness via promoting ensemble diversity"

Connected through hyperedge HE-3 (induced by source paper "adversarial robustness through local lipschitzness"):
  with members:
    - "randomized smoothing of all shapes and sizes"
    - "analyzing accuracy loss in randomized smoothing defenses"
    - "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
    - "rethinking randomized smoothing for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "theoretical evidence for adversarial robustness through randomization the case of the exponential family"

Connected through hyperedge HE-4 (induced by source paper "output diversified initialization for adversarial attacks"):
  with members:
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "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"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "towards sharper first order adversary with quantized gradients"):
  with members:
    - "mixtrain scalable training of formally robust neural networks"
    - "distributionally adversarial attack"
    - "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-6 (induced by source paper "smoothed inference for adversarially trained models"):
  with members:
    - "robust unsupervised neural machine translation with adversarial training"
    - "maxup a simple way to improve generalization of neural network training"
    - "randomized smoothing of all shapes and sizes"
    - "colored noise injection for training adversarially robust neural networks"
    - "enforcing linearity in dnn succours robustness and adversarial image generation"
    - "training robust deep neural networks via adversarial noise propagation"
    - "adversarial policies attacking deep reinforcement learning"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"

Connected through hyperedge HE-7 (induced by source paper "towards robust image classification using sequential attention models"):
  with members:
    - "natural adversarial examples"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "attention please adversarial defense via attention rectification and preservation"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "feature denoising for improving adversarial robustness"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial logit pairing"
    - "cram clued recurrent attention model"

Connected through hyperedge HE-8 (induced by source paper "are labels required for improving adversarial robustness"):
  with members:
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "unsupervised data augmentation for consistency training"
    - "semi supervised learning with scarce annotations"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

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)