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: "3d u net learning dense volumetric segmentation from sparse annotation"
  Abstract: This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.

Connected through hyperedge HE-1 (induced by source paper "convolutional occupancy networks"):
  with members:
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "learning shape templates with structured implicit functions"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "texture fields learning texture representations in function space"
    - "efficient learning on point clouds with basis point sets"
    - "pixel2mesh multi view 3d mesh generation via deformation"

Connected through hyperedge HE-2 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "medical image analysis using convolutional neural networks a review"
    - "skin lesion segmentation u nets versus clustering"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "the importance of skip connections in biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "pose aware instance segmentation framework from cone beam ct images for tooth segmentation"):
  with members:
    - "automatic registration between cone beam ct and scanned surface via deep pose regression neural networks and clustered similarities"
    - "yolov3 an incremental improvement"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "random erasing data augmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-4 (induced by source paper "unpaired multi modal segmentation via knowledge distillation"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "soft labeling by distilling anatomical knowledge for improved ms lesion segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "group normalization"
    - "self supervised knowledge distillation using singular value decomposition"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"

Connected through hyperedge HE-5 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "unet a nested u net architecture for medical image segmentation"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"

Connected through hyperedge HE-6 (induced by source paper "liver segmentation in abdominal ct images via auto context neural network and self supervised contour attention"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "deeply self supervising edge to contour neural network applied to liver segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "brain tumor segmentation with deep neural networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "recurrent convolutional neural networks for mandible segmentation from computed tomography"):
  with members:
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "combo loss handling input and output imbalance in multi organ segmentation"
    - "organ at risk segmentation in head and neck ct images by using a two stage segmentation framework based on 3d u net"
    - "attention u net learning where to look for the pancreas"
    - "ischemic stroke lesion segmentation in ct perfusion scans using pyramid pooling and focal loss"
    - "image segmentation algorithms overview"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"

Connected through hyperedge HE-8 (induced by source paper "deep non line of sight reconstruction"):
  with members:
    - "non line of sight reconstruction using efficient transient rendering"
    - "occupancy networks learning 3d reconstruction in function space"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "non line of sight imaging with partial occluders and surface normals"
    - "learning representations and generative models for 3d point clouds"
    - "fast back projection for non line of sight reconstruction"
    - "convolutional pose machines"
    - "learning a predictable and generative vector representation for objects"

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)