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: "untangling local and global deformations in deep convolutional networks for image classification and sliding window detection"
  Abstract: Deep Convolutional Neural Networks (DCNNs) commonly use generic 'max-pooling' (MP) layers to extract deformation-invariant features, but we argue in favor of a more refined treatment. First, we introduce epitomic con-volution as a building block alternative to the common convolution-MP cascade of DCNNs; while having identical complexity to MP, Epitomic Convolution allows for pa-rameter sharing across different filters, resulting in faster convergence and better generalization. Second, we introduce a Multiple Instance Learning approach to explicitly accommodate global translation and scaling when training a DCNN exclusively with class labels. For this we rely on a 'patchwork' data structure that efficiently lays out all image scales and positions as candidates to a DCNN. Factoring global and local deformations allows a DCNN to 'focus its resources' on the treatment of non-rigid defor-mations and yields a substantial classification accuracy improvement. Third, further pursuing this idea, we develop an efficient DCNN sliding window object detector that employs explicit search over position, scale, and aspect ratio. We provide competitive image classification and localization results on the ImageNet dataset and object detection results on the Pascal VOC 2007 benchmark.

Connected through hyperedge HE-1 (induced by source paper "weak supervision for generating pixel level annotations in scene text segmentation"):
  with members:
    - "coco_ts dataset pixel level annotations based on weak supervision for scene text segmentation"
    - "ocnet object context network for scene parsing"
    - "yolov3 an incremental improvement"
    - "dual attention network for scene segmentation"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "attention is all you need"
    - "squeeze and excitation networks"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "coco_ts dataset pixel level annotations based on weak supervision for scene text segmentation"):
  with members:
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "synthetic data for text localisation in natural images"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "synthcam3d semantic understanding with synthetic indoor scenes"

Connected through hyperedge HE-3 (induced by source paper "moe spnet a mixture of experts scene parsing network"):
  with members:
    - "loss max pooling for semantic image segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "semantic understanding of scenes through the ade20k dataset"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "interactive image segmentation using constrained dominant sets"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "semantic image segmentation via deep parsing network"

Connected through hyperedge HE-4 (induced by source paper "multi level contextual rnns with attention model for scene labeling"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "sanet structure aware network for visual tracking"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pixel recurrent neural networks"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "semantic image segmentation via deep parsing network"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "cnn based texture synthesize with semantic segment"):
  with members:
    - "identity mappings in deep residual networks"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "a neural algorithm of artistic style"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "draw a recurrent neural network for image generation"
    - "learning deconvolution network for semantic segmentation"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "signs in time encoding human motion as a temporal image"):
  with members:
    - "flowing convnets for human pose estimation in videos"
    - "two stream convolutional networks for action recognition in videos"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-7 (induced by source paper "attention to scale scale aware semantic image segmentation"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "holistically nested edge detection"
    - "joint object and part segmentation using deep learned potentials"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"
    - "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform"

Connected through hyperedge HE-8 (induced by source paper "convolutional channel features"):
  with members:
    - "multi view face detection using deep convolutional neural networks"
    - "filtered channel features for pedestrian detection"
    - "fast r cnn"
    - "boosting convolutional features for robust object proposals"
    - "taking a deeper look at pedestrians"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "densenet implementing efficient convnet descriptor pyramids"

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)