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: "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
  Abstract: Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks. Despite its success as a defense mechanism, adversarial training fails to generalize well to unperturbed test set. We hypothesize that this poor generalization is a consequence of adversarial training with uniform perturbation radius around every training sample. Samples close to decision boundary can be morphed into a different class under a small perturbation budget, and enforcing large margins around these samples produce poor decision boundaries that generalize poorly. Motivated by this hypothesis, we propose instance adaptive adversarial training -- a technique that enforces sample-specific perturbation margins around every training sample. We show that using our approach, test accuracy on unperturbed samples improve with a marginal drop in robustness. Extensive experiments on CIFAR-10, CIFAR-100 and Imagenet datasets demonstrate the effectiveness of our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "cat customized adversarial training for improved robustness"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "sign opt a query efficient hard label adversarial attack"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "certified adversarial robustness via randomized smoothing"
    - "convergence of adversarial training in overparametrized neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial robustness via adversarial label smoothing"

Connected through hyperedge HE-2 (induced by source paper "colored noise injection for training adversarially robust neural networks"):
  with members:
    - "training robust deep neural networks via adversarial noise propagation"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "adversarial training with voronoi constraints"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "learning to defense by learning to attack"
    - "efficient defenses against adversarial attacks"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-3 (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"
    - "certified adversarial robustness via randomized smoothing"

Connected through hyperedge HE-4 (induced by source paper "intriguing properties of adversarial training"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "convergence of adversarial training in overparametrized neural networks"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "are labels required for improving adversarial robustness"
    - "defending adversarial attacks by correcting logits"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial examples improve image recognition"

Connected through hyperedge HE-5 (induced by source paper "intriguing properties of adversarial training at scale"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "defending adversarial attacks by correcting logits"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial examples improve image recognition"
    - "adversarial training for free"
    - "defense against adversarial attacks using feature scattering based adversarial training"

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)