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: "deepunet a deep fully convolutional network for pixel level sea land segmentation"
  Abstract: Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for sea-land segmentation and the results could be further improved. This paper proposes a novel deep convolution neural network named DeepUNet. Like the U-Net, its structure has a contracting path and an expansive path to get high resolution output. But differently, the DeepUNet uses DownBlocks instead of convolution layers in the contracting path and uses UpBlock in the expansive path. The two novel blocks bring two new connections that are U-connection and Plus connection. They are promoted to get more precise segmentation results. To verify our network architecture, we made a new challenging sea-land dataset and compare the DeepUNet on it with the SegNet and the U-Net. Experimental results show that DeepUNet achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

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"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "an abstraction model for semantic segmentation algorithms"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "convolutional crfs for semantic segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-3 (induced by source paper "connection sensitive attention u net for accurate retinal vessel segmentation"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "decoupled weight decay regularization"
    - "iterative deep learning for network topology extraction"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "road extraction by deep residual u net"
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "a survey on deep learning in medical image analysis"
    - "improved inception residual convolutional neural network for object recognition"
    - "inception recurrent convolutional neural network for object recognition"

Connected through hyperedge HE-5 (induced by source paper "treesegnet adaptive tree cnns for subdecimeter aerial image segmentation"):
  with members:
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "distilling a neural network into a soft decision tree"
    - "rethinking atrous convolution for semantic image segmentation"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "conditional computation in neural networks for faster models"

Connected through hyperedge HE-6 (induced by source paper "feature fusion through multitask cnn for large scale remote sensing image segmentation"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deep residual learning for image recognition"

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)