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: "smoothed dilated convolutions for improved dense prediction"
  Abstract: Dilated convolutions, also known as atrous convolutions, have been widely explored in deep convolutional neural networks (DCNNs) for various tasks like semantic image segmentation, object detection, audio generation, video modeling, and machine translation. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance of DCNNs with dilated convolutions. In this work, we propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions. Unlike existing models, which explore solutions by focusing on a block of cascaded dilated convolutional layers, our methods address the gridding artifacts by smoothing the dilated convolution itself. By analyzing them in both the original operation and the decomposition views, we further point out that the two degridding approaches are intrinsically related and define separable and shared (SS) operations, which generalize the proposed methods. We evaluate our methods thoroughly on two datasets and visualize the smoothing effect through effective receptive field analysis. Experimental results show that our methods yield significant and consistent improvements on the performance of DCNNs with dilated convolutions, while adding negligible amounts of extra training parameters.

Connected through hyperedge HE-1 (induced by source paper "arma nets expanding receptive field for dense prediction"):
  with members:
    - "scale aware trident networks for object detection"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deformable convnets v2 more deformable better results"
    - "contextvp fully context aware video prediction"
    - "active convolution learning the shape of convolution for image classification"
    - "learning affinity via spatial propagation networks"
    - "deformable convolutional networks"
    - "dilated residual networks"

Connected through hyperedge HE-2 (induced by source paper "non local u net for biomedical image segmentation"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "non local neural networks"
    - "attention is all you need"
    - "identity mappings in deep residual networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "pay less attention with lightweight and dynamic convolutions"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "deblurring face images using uncertainty guided multi stream semantic networks"):
  with members:
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "deep semantic face deblurring"
    - "multi style generative network for real time transfer"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "cnn for license plate motion deblurring"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "efficient smoothing of dilated convolutions for image segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-6 (induced by source paper "gated context aggregation network for image dehazing and deraining"):
  with members:
    - "gated fusion network for single image dehazing"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "density aware single image de raining using a multi stream dense network"
    - "deep exemplar based colorization"
    - "decouple learning for parameterized image operators"
    - "densely connected pyramid dehazing network"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "benchmarking single image dehazing and beyond"

Connected through hyperedge HE-7 (induced by source paper "towards closing the gap in weakly supervised semantic segmentation with dcnns combining local and global models"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-8 (induced by source paper "global deep learning methods for multimodality isointense infant brain image segmentation"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "non local neural networks"
    - "attention is all you need"
    - "rethinking atrous convolution for semantic image segmentation"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "identity mappings in deep residual networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

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)