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: "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
  Abstract: We propose a novel deep layer cascade (LC) method to improve the accuracy and speed of semantic segmentation. Unlike the conventional model cascade (MC) that is composed of multiple independent models, LC treats a single deep model as a cascade of several sub-models. Earlier sub-models are trained to handle easy and confident regions, and they progressively feed-forward harder regions to the next sub-model for processing. Convolutions are only calculated on these regions to reduce computations. The proposed method possesses several advantages. First, LC classifies most of the easy regions in the shallow stage and makes deeper stage focuses on a few hard regions. Such an adaptive and 'difficulty-aware' learning improves segmentation performance. Second, LC accelerates both training and testing of deep network thanks to early decisions in the shallow stage. Third, in comparison to MC, LC is an end-to-end trainable framework, allowing joint learning of all sub-models. We evaluate our method on PASCAL VOC and Cityscapes datasets, achieving state-of-the-art performance and fast speed.

Connected through hyperedge HE-1 (induced by source paper "spgnet semantic prediction guidance for scene parsing"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "adversarial structure matching for structured prediction tasks"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "taskonomy disentangling task transfer learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "regularizing deep networks by modeling and predicting label structure"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "interpreting deep visual representations via network dissection"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "boundary aware feature propagation for scene segmentation"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "feature boosting network for 3d pose estimation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "s2dnas transforming static cnn model for dynamic inference via neural architecture search"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "sbnet sparse blocks network for fast inference"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "distributed iterative gating networks for semantic segmentation"):
  with members:
    - "gated multiple feedback network for image super resolution"
    - "recurrent iterative gating networks for semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "convolutional networks with adaptive inference graphs"
    - "iterative deep convolutional encoder decoder network for medical image segmentation"
    - "learning with rethinking recurrently improving convolutional neural networks through feedback"

Connected through hyperedge HE-7 (induced by source paper "efficient semantic scene completion network with spatial group convolution"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "inference learning and attention mechanisms that exploit and preserve sparsity in convolutional networks"
    - "3dcnn dqn rnn a deep reinforcement learning framework for semantic parsing of large scale 3d point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-8 (induced by source paper "context integrated and feature refined network for lightweight object parsing"):
  with members:
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rtseg real time semantic segmentation comparative study"
    - "image super resolution using very deep residual channel attention networks"
    - "guided upsampling network for real time semantic segmentation"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"

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)