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: "varmixup exploiting the latent space for robust training and inference"
  Abstract: The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has led to the development of many defense approaches. Among them, Adversarial Training (AT) is a popular and widely used approach for training adversarially robust models. Mixup Training (MT), a recent popular training algorithm, improves the generalization performance of models by introducing globally linear behavior in between training examples. Although still in its early phase, we observe a shift in trend of exploiting Mixup from perspectives of generalisation to that of adversarial robustness. It has been shown that the Mixup trained models improves the robustness of models but only passively. A recent approach, Mixup Inference (MI), proposes an inference principle for Mixup trained models to counter adversarial examples at inference time by mixing the input with other random clean samples. In this work, we propose a new approach - \textit{VarMixup (Variational Mixup)} - to better sample mixup images by using the latent manifold underlying the data. Our experiments on CIFAR-10, CIFAR-100, SVHN and Tiny-Imagenet demonstrate that \textit{VarMixup} beats state-of-the-art AT techniques without training the model adversarially. Additionally, we also conduct ablations that show that models trained on \textit{VarMixup} samples are also robust to various input corruptions/perturbations, have low calibration error and are transferable.

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)