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: "does data augmentation lead to positive margin"
  Abstract: Data augmentation (DA) is commonly used during model training, as it significantly improves test error and model robustness. DA artificially expands the training set by applying random noise, rotations, crops, or even adversarial perturbations to the input data. Although DA is widely used, its capacity to provably improve robustness is not fully understood. In this work, we analyze the robustness that DA begets by quantifying the margin that DA enforces on empirical risk minimizers. We first focus on linear separators, and then a class of nonlinear models whose labeling is constant within small convex hulls of data points. We present lower bounds on the number of augmented data points required for non-zero margin, and show that commonly used DA techniques may only introduce significant margin after adding exponentially many points to the data set.

Connected through hyperedge HE-1 (induced by source paper "convergence and margin of adversarial training on separable data"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "universal adversarial training"
    - "adversarially robust generalization requires more data"
    - "risk and parameter convergence of logistic regression"

Connected through hyperedge HE-2 (induced by source paper "achieving generalizable robustness of deep neural networks by stability training"):
  with members:
    - "learning optimal data augmentation policies via bayesian optimization for image classification tasks"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"
    - "methods for interpreting and understanding deep neural networks"
    - "autoaugment learning augmentation policies from data"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "improving deep learning using generic data augmentation"

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)