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: "dense volume to volume vascular boundary detection"
  Abstract: In this work, we present a novel 3D-Convolutional Neural Network (CNN) architecture called I2I-3D that predicts boundary location in volumetric data. Our fine-to-fine, deeply supervised framework addresses three critical issues to 3D boundary detection: (1) efficient, holistic, end-to-end volumetric label training and prediction (2) precise voxel-level prediction to capture fine scale structures prevalent in medical data and (3) directed multi-scale, multi-level feature learning. We evaluate our approach on a dataset consisting of 93 medical image volumes with a wide variety of anatomical regions and vascular structures. In the process, we also introduce HED-3D, a 3D extension of the state-of-the-art 2D edge detector (HED). We show that our deep learning approach out-performs, the current state-of-the-art in 3D vascular boundary detection (structured forests 3D), by a large margin, as well as HED applied to slices, and HED-3D while successfully localizing fine structures. With our approach, boundary detection takes about one minute on a typical 512x512x512 volume.

Connected through hyperedge HE-1 (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-2 (induced by source paper "upi net semantic contour detection in placental ultrasound"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "bam bottleneck attention module"
    - "group normalization"
    - "attention guided unified network for panoptic segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-3 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "deepflux for skeletons in the wild"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"

Connected through hyperedge HE-4 (induced by source paper "training multi organ segmentation networks with sample selection by relaxed upper confident bound"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "training region based object detectors with online hard example mining"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "kid net convolution networks for kidney vessels segmentation from ct volumes"):
  with members:
    - "one model to learn them all"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "semantic segmentation with boundary neural fields"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "instance level salient object segmentation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "voxresnet deep voxelwise residual networks for volumetric brain 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"

Connected through hyperedge HE-8 (induced by source paper "iterative deep learning for network topology extraction"):
  with members:
    - "roomnet end to end room layout estimation"
    - "stacked hourglass networks for human pose estimation"
    - "torontocity seeing the world with a million eyes"
    - "fully convolutional instance aware semantic segmentation"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale 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)