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: "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.

Connected through hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"

Connected through hyperedge HE-2 (induced by source paper "topologically densified distributions"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "utilizing class information for deep network representation shaping"
    - "augment your batch better training with larger batches"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a topological regularizer for classifiers via persistent homology"

Connected through hyperedge HE-3 (induced by source paper "neural kernels without tangents"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "do imagenet classifiers generalize to imagenet"
    - "on exact computation with an infinitely wide neural net"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"

Connected through hyperedge HE-4 (induced by source paper "restyling data application to unsupervised domain adaptation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "artistic object recognition by unsupervised style adaptation"
    - "style augmentation data augmentation via style randomization"
    - "image to image translation for domain adaptation"
    - "no more discrimination cross city adaptation of road scene segmenters"

Connected through hyperedge HE-5 (induced by source paper "improving robustness without sacrificing accuracy with patch gaussian augmentation"):
  with members:
    - "using videos to evaluate image model robustness"
    - "adversarial examples are a natural consequence of test error in noise"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning robust representations by projecting superficial statistics out"
    - "do imagenet classifiers generalize to imagenet"
    - "using pre training can improve model robustness and uncertainty"
    - "dropblock a regularization method for convolutional networks"
    - "benchmarking neural network robustness to common corruptions and surface variations"

Connected through hyperedge HE-6 (induced by source paper "data augmentation revisited rethinking the distribution gap between clean and augmented data"):
  with members:
    - "implicit semantic data augmentation for deep networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "fast autoaugment"
    - "bag of tricks for image classification with convolutional neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "a shape transformation based dataset augmentation framework for pedestrian detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "learning to generate long term future via hierarchical prediction"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"

Connected through hyperedge HE-8 (induced by source paper "a hessian based complexity measure for deep networks"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "two models of double descent for weak features"
    - "how do infinite width bounded norm networks look in function space"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "stronger generalization bounds for deep nets via a compression approach"

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)