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: "reseg a recurrent neural network based model for semantic segmentation"
  Abstract: We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on this https URL

Connected through hyperedge HE-1 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep depth completion of a single rgb d image"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "a survey on deep learning based architectures for semantic segmentation on 2d images"):
  with members:
    - "panoptic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "semantic segmentation of video sequences with convolutional lstms"
    - "yolact real time instance segmentation"
    - "convolutional crfs for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic video segmentation a review on recent approaches"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-3 (induced by source paper "collaborative global local networks for memory efficient segmentation of ultra high resolution images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "an abstraction model for semantic segmentation algorithms"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "convolutional crfs for semantic segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"

Connected through hyperedge HE-5 (induced by source paper "fast scnn fast semantic segmentation network"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "document structure extraction for forms using very high resolution semantic segmentation"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "multimodal deep networks for text and image based document classification"
    - "rethinking table parsing using graph neural networks"
    - "extracting scientific figures with distantly supervised neural networks"
    - "chargrid towards understanding 2d documents"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "a methodological review of visual road recognition procedures for autonomous driving applications"):
  with members:
    - "fast lidar based road detection using fully convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning a driving simulator"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-8 (induced by source paper "dense recurrent neural networks for scene labeling"):
  with members:
    - "multi context attention for human pose estimation"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "scene parsing with global context embedding"
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "hierarchical question image co attention for visual question answering"

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)