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: "s3pool pooling with stochastic spatial sampling"
  Abstract: Feature pooling layers (e.g., max pooling) in convolutional neural networks (CNNs) serve the dual purpose of providing increasingly abstract representations as well as yielding computational savings in subsequent convolutional layers. We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e.g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e.g., top-left) manner. Our starting point in this work is the observation that this regularly spaced downsampling arising from non-overlapping windows, although intuitive from a signal processing perspective (which has the goal of signal reconstruction), is not necessarily optimal for \emph{learning} (where the goal is to generalize). We study this aspect and propose a novel pooling strategy with stochastic spatial sampling (S3Pool), where the regular downsampling is replaced by a more general stochastic version. We observe that this general stochasticity acts as a strong regularizer, and can also be seen as doing implicit data augmentation by introducing distortions in the feature maps. We further introduce a mechanism to control the amount of distortion to suit different datasets and architectures. To demonstrate the effectiveness of the proposed approach, we perform extensive experiments on several popular image classification benchmarks, observing excellent improvements over baseline models. Experimental code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning"):
  with members:
    - "panoptic segmentation"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-2 (induced by source paper "stochastic region pooling make attention more expressive"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi attention multi class constraint for fine grained image recognition"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "learning what and where to attend"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "lip local importance based pooling"):
  with members:
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "scale aware trident networks for object detection"
    - "bag of tricks for image classification with convolutional neural networks"
    - "detnet a backbone network for object detection"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "detail preserving pooling in deep networks"

Connected through hyperedge HE-4 (induced by source paper "radius adaptive convolutional neural network"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "searching for mobilenetv3"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "a hybrid approach with optimization and metric based meta learner for few shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "few shot learning with meta metric learners"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "bayesian model agnostic meta learning"
    - "deep meta learning learning to learn in the concept space"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "diverse few shot text classification with multiple metrics"

Connected through hyperedge HE-6 (induced by source paper "mixed supervised dual network for medical image segmentation"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "annotation cost minimization for medical image segmentation using suggestive mixed supervision fully convolutional networks"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "deep learning with mixed supervision for brain tumor segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"

Connected through hyperedge HE-7 (induced by source paper "universal pooling a new pooling method for convolutional neural networks"):
  with members:
    - "enhancing operation of a sewage pumping station for inter catchment wastewater transfer by using deep learning and hydraulic model"
    - "detail preserving pooling in deep networks"
    - "cbam convolutional block attention module"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "parallel grid pooling for data augmentation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "feature space transfer for data augmentation"
    - "shakedrop regularization for deep residual learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "random erasing data augmentation"
    - "learning transferable architectures for scalable image recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"

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)