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: "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
  Abstract: Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e.g., object detection and segmentation. However, how to fully leverage contextual representations for recognition tasks from volumetric data has not been well studied, especially in the field of medical image computing, where a majority of image modalities are in volumetric format. In this paper we explore the deep residual learning on the task of volumetric brain segmentation. There are at least two main contributions in our work. First, we propose a deep voxelwise residual network, referred as VoxResNet, which borrows the spirit of deep residual learning in 2D image recognition tasks, and is extended into a 3D variant for handling volumetric data. Second, an auto-context version of VoxResNet is proposed by seamlessly integrating the low-level image appearance features, implicit shape information and high-level context together for further improving the volumetric segmentation performance. Extensive experiments on the challenging benchmark of brain segmentation from magnetic resonance (MR) images corroborated the efficacy of our proposed method in dealing with volumetric data. We believe this work unravels the potential of 3D deep learning to advance the recognition performance on volumetric image segmentation.

Connected through hyperedge HE-1 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "a survey on deep transfer learning"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "deep contextualized word representations"
    - "video salient object detection via fully convolutional networks"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a survey on 3d inner structure prediction from its outer shape"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-3 (induced by source paper "ce net context encoder network for 2d medical image segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "infinet fully convolutional networks for infant brain mri segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "sparse range constrained learning and its application for medical image grading"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "pathological oct retinal layer segmentation using branch residual u shape networks"

Connected through hyperedge HE-4 (induced by source paper "cascade decoder a universal decoding method for biomedical image segmentation"):
  with members:
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "a new registration approach for dynamic analysis of calcium signals in organs"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "neuron segmentation using deep complete bipartite networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "tangent convolutions for dense prediction in 3d"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octnetfusion learning depth fusion from data"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "learning compact geometric features"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-6 (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"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "a survey on deep learning in medical image analysis"
    - "improved inception residual convolutional neural network for object recognition"

Connected through hyperedge HE-7 (induced by source paper "medical image analysis using convolutional neural networks a review"):
  with members:
    - "unet a nested u net architecture for medical image segmentation"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "brain tumor segmentation with deep neural networks"
    - "adversarial discriminative domain adaptation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "medical image retrieval using deep convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "an application of cascaded 3d fully convolutional networks for medical image segmentation"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image 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)