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: "label refinement network for coarse to fine semantic segmentation"
  Abstract: We consider the problem of semantic image segmentation using deep convolutional neural networks. We propose a novel network architecture called the label refinement network that predicts segmentation labels in a coarse-to-fine fashion at several resolutions. The segmentation labels at a coarse resolution are used together with convolutional features to obtain finer resolution segmentation labels. We define loss functions at several stages in the network to provide supervisions at different stages. Our experimental results on several standard datasets demonstrate that the proposed model provides an effective way of producing pixel-wise dense image labeling.

Connected through hyperedge HE-1 (induced by source paper "deepdualmapper a gated fusion network for automatic map extraction using aerial images and trajectories"):
  with members:
    - "leveraging crowdsourced gps data for road extraction from aerial imagery"
    - "multi mathbf 3 net segmenting flooded buildings via fusion of multiresolution multisensor and multitemporal satellite imagery"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "object contextual representations for semantic segmentation"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "acfnet attentional class feature network for semantic segmentation"
    - "dual graph convolutional network for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "global aggregation then local distribution in fully convolutional networks"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"

Connected through hyperedge HE-3 (induced by source paper "acfnet attentional class feature network for semantic segmentation"):
  with members:
    - "shapemask learning to segment novel objects by refining shape priors"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "autofocus layer for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-4 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching"):
  with members:
    - "amnet deep atrous multiscale stereo disparity estimation networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "single image depth estimation by dilated deep residual convolutional neural network and soft weight sum inference"

Connected through hyperedge HE-5 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "interpretable structure evolving lstm"
    - "instance level salient object segmentation"
    - "semantic instance segmentation via deep metric learning"

Connected through hyperedge HE-6 (induced by source paper "recurrent iterative gating networks for semantic segmentation"):
  with members:
    - "gated feedback refinement network for coarse to fine dense semantic image labeling"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "learning with rethinking recurrently improving convolutional neural networks through feedback"
    - "recurrent human pose estimation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"
    - "geometric scene parsing with hierarchical lstm"
    - "residual networks behave like ensembles of relatively shallow networks"

Connected through hyperedge HE-7 (induced by source paper "pyramid stereo matching network"):
  with members:
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "stacked deconvolutional network for semantic segmentation"
    - "learning for disparity estimation through feature constancy"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-8 (induced by source paper "relative saliency and ranking models metrics data and benchmarks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep learning is robust to massive label noise"
    - "gated feedback refinement network for coarse to fine dense semantic image labeling"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "towards the success rate of one real time unconstrained salient object detection"
    - "structure measure a new way to evaluate foreground maps"

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)