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: "a3t adversarially augmented adversarial training"
  Abstract: Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier. Most classification models, including deep learning models, are highly vulnerable to adversarial attacks. In this work, we investigate a procedure to improve adversarial robustness of deep neural networks through enforcing representation invariance. The idea is to train the classifier jointly with a discriminator attached to one of its hidden layer and trained to filter the adversarial noise. We perform preliminary experiments to test the viability of the approach and to compare it to other standard adversarial training methods.

Connected through hyperedge HE-1 (induced by source paper "defense pointnet protecting pointnet against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating 3d adversarial point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the sensitivity of adversarial robustness to input data distributions"):
  with members:
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "adversarially robust generalization requires more data"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "synthesizing robust adversarial examples"
    - "shake shake regularization"

Connected through hyperedge HE-3 (induced by source paper "a d net training a shadow detector with adversarial shadow attenuation"):
  with members:
    - "direction aware spatial context features for shadow detection"
    - "mixup beyond empirical risk minimization"
    - "adversarial feature augmentation for unsupervised domain adaptation"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "adversarial examples for semantic segmentation and object detection"
    - "learning from simulated and unsupervised images through adversarial training"
    - "universal adversarial perturbations"
    - "image to image translation with conditional adversarial 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.LG(Machine Learning)