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: "exfuse enhancing feature fusion for semantic segmentation"
  Abstract: Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spatial resolution. We find that introducing semantic information into low-level features and high-resolution details into high-level features is more effective for the later fusion. Based on this observation, we propose a new framework, named ExFuse, to bridge the gap between low-level and high-level features thus significantly improve the segmentation quality by 4.0\% in total. Furthermore, we evaluate our approach on the challenging PASCAL VOC 2012 segmentation benchmark and achieve 87.9\% mean IoU, which outperforms the previous state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "esfnet efficient network for building extraction from high resolution aerial images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "interleaved group convolutions for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "task decomposition and synchronization for semantic biomedical image segmentation"):
  with members:
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "a survey on multi task learning"

Connected through hyperedge HE-3 (induced by source paper "boundary aware network for fast and high accuracy portrait segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic human matting"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "fast deep matting for portrait animation on mobile phone"
    - "real time deep hair matting on mobile devices"
    - "deep image matting"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "design of real time semantic segmentation decoder for automated driving"):
  with members:
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "feature fusion encoder decoder network for automatic liver lesion segmentation"):
  with members:
    - "detection aided liver lesion segmentation using deep learning"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "squeeze and excitation networks"
    - "automatic liver lesion detection using cascaded deep residual networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "3d deeply supervised network for automatic liver segmentation from ct volumes"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"

Connected through hyperedge HE-6 (induced by source paper "residual pyramid fcn for robust follicle segmentation"):
  with members:
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "dilated residual networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for 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)