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: "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
  Abstract: Recent semantic segmentation methods exploit encoder-decoder architectures to produce the desired pixel-wise segmentation prediction. The last layer of the decoders is typically a bilinear upsampling procedure to recover the final pixel-wise prediction. We empirically show that this oversimple and data-independent bilinear upsampling may lead to sub-optimal results. #R##N#In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs. The main advantage of the new upsampling layer lies in that with a relatively lower-resolution feature map such as $\frac{1}{16}$ or $\frac{1}{32}$ of the input size, we can achieve even better segmentation accuracy, significantly reducing computation complexity. This is made possible by 1) the new upsampling layer's much improved reconstruction capability; and more importantly 2) the DUpsampling based decoder's flexibility in leveraging almost arbitrary combinations of the CNN encoders' features. Experiments demonstrate that our proposed decoder outperforms the state-of-the-art decoder, with only $\sim$20\% of computation. Finally, without any post-processing, the framework equipped with our proposed decoder achieves new state-of-the-art performance on two datasets: 88.1\% mIOU on PASCAL VOC with 30\% computation of the previously best model; and 52.5\% mIOU on PASCAL Context.

Connected through hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "efficient semantic video segmentation with per frame inference"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "deep video inpainting"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"
    - "deep spatio temporal random fields for efficient video segmentation"

Connected through hyperedge HE-3 (induced by source paper "pixel level dense prediction without decoder"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-4 (induced by source paper "flattenet a simple versatile framework for dense pixelwise prediction"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "high resolution representations for labeling pixels and regions"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-5 (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"
    - "high resolution representations for labeling pixels and regions"
    - "semantic correlation promoted shape variant context for segmentation"

Connected through hyperedge HE-6 (induced by source paper "location aware upsampling for semantic segmentation"):
  with members:
    - "fastfcn rethinking dilated convolution in the backbone for semantic segmentation"
    - "high resolution representations for labeling pixels and regions"
    - "boundary aware feature propagation for scene segmentation"
    - "difnet semantic segmentation by diffusion networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deformable convnets v2 more deformable better results"
    - "dual attention network for scene segmentation"
    - "context encoding for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "fcos fully convolutional one stage object detection"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "feature selective anchor free module for single shot object detection"
    - "deep high resolution representation learning for human pose estimation"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "an end to end textspotter with explicit alignment and attention"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-8 (induced by source paper "enforcing geometric constraints of virtual normal for depth prediction"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "fcos fully convolutional one stage object detection"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking monocular depth estimation with adversarial training"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep ordinal regression network for monocular depth estimation"

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)