human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a kernel theory of modern data augmentation"):
  Abstract: Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding modern data augmentation techniques. We start by showing that for kernel classifiers, data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. We connect this general approximation framework to prior work in invariant kernels, tangent propagation, and robust optimization. Next, we explicitly tackle the compositional aspect of modern data augmentation techniques, proposing a novel model of data augmentation as a Markov process. Under this model, we show that performing $k$-nearest neighbors with data augmentation is asymptotically equivalent to a kernel classifier. Finally, we illustrate ways in which our theoretical framework can be leveraged to accelerate machine learning workflows in practice, including reducing the amount of computation needed to train on augmented data, and predicting the utility of a transformation prior to training.

Includes members:
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "max margin invariant features from transformed unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non parametric transformation networks")
  Member "local group invariant representations via orbit embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalization properties of hyper rkhs and its application to out of sample extensions")
  Member "understanding deep learning requires rethinking generalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "regularization with stochastic transformations and perturbations for deep semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "fractional max pooling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pointaugment an auto augmentation framework for point cloud classification")
  Member "discriminative unsupervised feature learning with exemplar convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

Question: Please tell me which class the source paper 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)