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: "adversarial training versus weight decay"
  Abstract: Performance-critical machine learning models should be robust to input perturbations not seen during training. Adversarial training is a method for improving a model's robustness to some perturbations by including them in the training process, but this tends to exacerbate other vulnerabilities of the model. The adversarial training framework has the effect of translating the data with respect to the cost function, while weight decay has a scaling effect. Although weight decay could be considered a crude regularization technique, it appears superior to adversarial training as it remains stable over a broader range of regimes and reduces all generalization errors. Equipped with these abstractions, we provide key baseline results and methodology for characterizing robustness. The two approaches can be combined to yield one small model that demonstrates good robustness to several white-box attacks associated with different metrics.

Connected through hyperedge HE-1 (induced by source paper "robust deep neural networks inspired by fuzzy logic"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"
    - "understanding adversarial robustness the trade off between minimum and average margin"
    - "sparse dnns with improved adversarial robustness"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "adversarially robust generalization requires more data"
    - "provable robustness of relu networks via maximization of linear regions"

Connected through hyperedge HE-2 (induced by source paper "batch normalization is a cause of adversarial vulnerability"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a mean field theory of batch normalization"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-3 (induced by source paper "built in vulnerabilities to imperceptible adversarial perturbations"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial spheres"
    - "adversarially robust generalization requires more data"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples as an input fault tolerance problem"):
  with members:
    - "spatially transformed adversarial examples"
    - "towards the first adversarially robust neural network model on mnist"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "adversarially robust generalization requires more data"
    - "constructing unrestricted adversarial examples with generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "motivating the rules of the game for adversarial example research"
    - "exploring the landscape of spatial robustness"

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)