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: "pixelnet towards a general pixel level architecture"
  Abstract: We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that (1) stratified sampling allows us to add diversity during batch updates and (2) sampled multi-scale features allow us to explore more nonlinear predictors (multiple fully-connected layers followed by ReLU) that improve overall accuracy. Finally, our objective is to show how a architecture can get performance better than (or comparable to) the architectures designed for a particular task. Interestingly, our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context, surface normal estimation on NYUDv2 dataset, and edge detection on BSDS without contextual post-processing.

Connected through hyperedge HE-1 (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-2 (induced by source paper "self supervised learning for single view depth and surface normal estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "lego learning edge with geometry all at once by watching videos"
    - "unsupervised learning of depth and ego motion from video"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"
    - "unsupervised learning of geometry with edge aware depth normal consistency"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-3 (induced by source paper "hierarchical transfer convolutional neural networks for image classification"):
  with members:
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "resnet in resnet generalizing residual architectures"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "what makes imagenet good for transfer learning"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "cnn rnn a unified framework for multi label image classification"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "subsurface structure analysis using computational interpretation and learning a visual signal processing perspective"):
  with members:
    - "salsi a new seismic attribute for salt dome detection"
    - "the role of visual saliency in the automation of seismic interpretation"
    - "lstm a search space odyssey"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"

Connected through hyperedge HE-5 (induced by source paper "exploiting saliency for object segmentation from image level labels"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convexification of learning from constraints"
    - "deep contrast learning for salient object detection"
    - "improving weakly supervised object localization by micro annotation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "beyond forward shortcuts fully convolutional master slave networks msnets with backward skip connections for semantic segmentation"):
  with members:
    - "resnet in resnet generalizing residual architectures"
    - "semantic understanding of scenes through the ade20k dataset"
    - "bridging category level and instance level semantic image segmentation"
    - "deep networks with stochastic depth"
    - "the importance of skip connections in biomedical image segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"):
  with members:
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "understanding convolution for semantic segmentation"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "automatic building extraction in aerial scenes using convolutional networks"
    - "bottom up instance segmentation using deep higher order crfs"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-8 (induced by source paper "loss max pooling for semantic image segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "joint calibration for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"

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)