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: "harnessing the vulnerability of latent layers in adversarially trained models"
  Abstract: Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective method of defending against these adversarial attacks is to use the methodology of adversarial training. We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers. Our analysis reveals that contrary to the input layer which is robust to adversarial attack, the latent layer of these robust models are highly susceptible to adversarial perturbations of small magnitude. Leveraging this information, we introduce a new technique Latent Adversarial Training (LAT) which comprises of fine-tuning the adversarially trained models to ensure the robustness at the feature layers. We also propose Latent Attack (LA), a novel algorithm for construction of adversarial examples. LAT results in minor improvement in test accuracy and leads to a state-of-the-art adversarial accuracy against the universal first-order adversarial PGD attack which is shown for the MNIST, CIFAR-10, CIFAR-100 datasets.

Connected through hyperedge HE-1 (induced by source paper "varmixup exploiting the latent space for robust training and inference"):
  with members:
    - "adversarial domain adaptation with domain mixup"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"
    - "spatially transformed 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"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "jacobian adversarially regularized networks for robustness"

Connected through hyperedge HE-3 (induced by source paper "on the benefits of attributional robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based dropout layer for weakly supervised object localization"
    - "robust attribution regularization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-4 (induced by source paper "white box adversarial defense via self supervised data estimation"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "deflecting adversarial attacks with pixel deflection"
    - "recent advances in autoencoder based representation learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "enforcing linearity in dnn succours robustness and adversarial image generation"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"
    - "deep image prior"
    - "countering adversarial images using input transformations"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "advknn adversarial attacks on k nearest neighbor classifiers with approximate gradients"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "defending against adversarial examples with k nearest neighbor"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "countering adversarial images using input transformations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "advgan harnessing latent layers for adversary generation"):
  with members:
    - "targeted adversarial examples for black box audio systems"
    - "generating adversarial examples with adversarial networks"
    - "adaptive adversarial attack on scene text recognition"
    - "adversarial attacks on neural network policies"
    - "adversarial examples for semantic segmentation and object detection"
    - "image to image translation with conditional adversarial networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

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)