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: "high performance semantic segmentation using very deep fully convolutional networks"
  Abstract: We propose a method for high-performance semantic image segmentation (or semantic pixel labelling) based on very deep residual networks, which achieves the state-of-the-art performance. A few design factors are carefully considered to this end. We make the following contributions. (i) First, we evaluate different variations of a fully convolutional residual network so as to find the best configuration, including the number of layers, the resolution of feature maps, and the size of field-of-view. Our experiments show that further enlarging the field-of-view and increasing the resolution of feature maps are typically beneficial, which however inevitably leads to a higher demand for GPU memories. To walk around the limitation, we propose a new method to simulate a high resolution network with a low resolution network, which can be applied during training and/or testing. (ii) Second, we propose an online bootstrapping method for training. We demonstrate that online bootstrapping is critically important for achieving good accuracy. (iii) Third we apply the traditional dropout to some of the residual blocks, which further improves the performance. (iv) Finally, our method achieves the currently best mean intersection-over-union 78.3% on the PASCAL VOC 2012 dataset, as well as on the recent dataset Cityscapes. ∗This research was in part supported by the Data to Decisions Cooperative Research Centre. C. Shen’s participation was in part supported by an ARC Future Fellowship (FT120100969). C. Shen is the corresponding author. 1 ar X iv :1 60 4. 04 33 9v 1 [ cs .C V ] 1 5 A pr 2 01 6

Connected through hyperedge HE-1 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks"):
  with members:
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "efficient segmentation learning downsampling near semantic boundaries"
    - "boundary aware feature propagation for scene segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "real time high performance semantic image segmentation of urban street scenes"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "lednet a lightweight encoder decoder network for real time semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "yolov3 an incremental improvement"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "gated path selection network for semantic segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "ccnet criss cross attention for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deformable convnets v2 more deformable better results"
    - "deformable convolutional networks"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "seamless scene segmentation"):
  with members:
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "learning to fuse things and stuff"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-5 (induced by source paper "rgpnet a real time general purpose semantic segmentation"):
  with members:
    - "high resolution representations for labeling pixels and regions"
    - "fast scnn fast semantic segmentation network"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "person search by multi scale matching"

Connected through hyperedge HE-6 (induced by source paper "graph guided architecture search for real time semantic segmentation"):
  with members:
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "reinforced evolutionary neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-7 (induced by source paper "casenet content adaptive scale interaction networks for scene parsing"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "devil in the details towards accurate single and multiple human parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "context aware visual policy network for sequence level image captioning"
    - "unified perceptual parsing for scene understanding"

Connected through hyperedge HE-8 (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"

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)