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: "learning to zoom a saliency based sampling layer for neural networks"
  Abstract: We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer can be added as a preprocessing block to existing task networks and trained altogether in an end-to-end fashion. The effect of the layer is to efficiently estimate how to sample from the original data in order to boost task performance. For example, for an image classification task in which the original data might range in size up to several megapixels, but where the desired input images to the task network are much smaller, our layer learns how best to sample from the underlying high resolution data in a manner which preserves task-relevant information better than uniform downsampling. This has the effect of creating distorted, caricature-like intermediate images, in which idiosyncratic elements of the image that improve task performance are zoomed and exaggerated. Unlike alternative approaches such as spatial transformer networks, our proposed layer is inspired by image saliency, computed efficiently from uniformly downsampled data, and degrades gracefully to a uniform sampling strategy under uncertainty. We apply our layer to improve existing networks for the tasks of human gaze estimation and fine-grained object classification. Code for our method is available in: this http URL

Connected through hyperedge HE-1 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "transgaga geometry aware unsupervised image to image translation"
    - "emergence of object segmentation in perturbed generative models"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "learning conditional deformable templates with convolutional networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "voxelmorph a learning framework for deformable medical image registration"

Connected through hyperedge HE-2 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "learn to pay attention"
    - "attention is all you need"
    - "deep layer aggregation"

Connected through hyperedge HE-3 (induced by source paper "beyond the camera neural networks in world coordinates"):
  with members:
    - "deep online video stabilization"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "what actions are needed for understanding human actions in videos"
    - "deformable convolutional networks"
    - "sfm net learning of structure and motion from video"
    - "dynamic computational time for visual attention"

Connected through hyperedge HE-4 (induced by source paper "autoscale learning to scale for crowd counting"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "point in box out beyond counting persons in crowds"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "autofocus efficient multi scale inference"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-5 (induced by source paper "efficient segmentation learning downsampling near semantic boundaries"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "active convolution learning the shape of convolution for image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "processing megapixel images with deep attention sampling models"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "neural image compression for gigapixel histopathology image analysis"
    - "attention based deep multiple instance learning"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-7 (induced by source paper "learning task specific generalized convolutions in the permutohedral lattice"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic filtering with large sampling field for convnets"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"
    - "segmentation aware convolutional networks using local attention masks"
    - "sampling matters in deep embedding learning"
    - "non local neural networks"

Connected through hyperedge HE-8 (induced by source paper "3d neighborhood convolution learning depth aware features for rgb d and rgb semantic segmentation"):
  with members:
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth aware cnn for rgb d segmentation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "deformable convnets v2 more deformable better results"
    - "non local neural networks"
    - "deformable convolutional networks"

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.CV(Computer Vision and Pattern Recognition)