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: "stacked deconvolutional network for semantic segmentation"
  Abstract: Recent progress in semantic segmentation has been driven by improving the spatial resolution under Fully Convolutional Networks (FCNs). To address this problem, we propose a Stacked Deconvolutional Network (SDN) for semantic segmentation. In SDN, multiple shallow deconvolutional networks, which are called as SDN units, are stacked one by one to integrate contextual information and guarantee the fine recovery of localization information. Meanwhile, inter-unit and intra-unit connections are designed to assist network training and enhance feature fusion since the connections improve the flow of information and gradient propagation throughout the network. Besides, hierarchical supervision is applied during the upsampling process of each SDN unit, which guarantees the discrimination of feature representations and benefits the network optimization. We carry out comprehensive experiments and achieve the new state-of-the-art results on three datasets, including PASCAL VOC 2012, CamVid, GATECH. In particular, our best model without CRF post-processing achieves an intersection-over-union score of 86.6% in the test set.

Connected through hyperedge HE-1 (induced by source paper "deeperlab single shot image parser"):
  with members:
    - "panoptic segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "affinity derivation and graph merge for instance segmentation"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "sparsemask differentiable connectivity learning for dense image prediction"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "sparsely aggregated convolutional networks"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "maskconnect connectivity learning by gradient descent"

Connected through hyperedge HE-3 (induced by source paper "segsort segmentation by discriminative sorting of segments"):
  with members:
    - "adversarial structure matching for structured prediction tasks"
    - "deeperlab single shot image parser"
    - "efficient parameter free clustering using first neighbor relations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "cosface large margin cosine loss for deep face recognition"
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image 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"
    - "label refinement network for coarse to fine semantic segmentation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-5 (induced by source paper "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "the devil is in the decoder classification regression and gans"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bridging category level and instance level semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "correlation maximized structural similarity loss for semantic segmentation"):
  with members:
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "focal loss for dense object detection"
    - "learning affinity via spatial propagation networks"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "on loss functions for deep neural networks in classification"

Connected through hyperedge HE-7 (induced by source paper "adaptive context network for scene parsing"):
  with members:
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "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"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-8 (induced by source paper "dsnet an efficient cnn for road scene segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"

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)