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: "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
  Abstract: Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics. An important computation for reconstruction is the detection of neuronal boundaries. Images acquired by serial section EM, a leading 3D EM technique, are highly anisotropic, with inferior quality along the third dimension. For such images, the 2D max-pooling convolutional network has set the standard for performance at boundary detection. Here we achieve a substantial gain in accuracy through three innovations. Following the trend towards deeper networks for object recognition, we use a much deeper network than previously employed for boundary detection. Second, we incorporate 3D as well as 2D filters, to enable computations that use 3D context. Finally, we adopt a recursively trained architecture in which a first network generates a preliminary boundary map that is provided as input along with the original image to a second network that generates a final boundary map. Backpropagation training is accelerated by ZNN, a new implementation of 3D convolutional networks that uses multicore CPU parallelism for speed. Our hybrid 2D-3D architecture could be more generally applicable to other types of anisotropic 3D images, including video, and our recursive framework for any image labeling problem.

Connected through hyperedge HE-1 (induced by source paper "3d dense separated convolution module for volumetric image analysis"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "a new ensemble learning framework for 3d biomedical image segmentation"
    - "s4nd single shot single scale lung nodule detection"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "densely connected convolutional networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-2 (induced by source paper "spda superpixel based data augmentation for biomedical image segmentation"):
  with members:
    - "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"
    - "low shot learning from imaginary data"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "mixup beyond empirical risk minimization"
    - "gland instance segmentation using deep multichannel neural networks"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-3 (induced by source paper "estimation of the volume of the left ventricle from mri images using deep neural networks"):
  with members:
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri"
    - "learning deconvolution network for semantic segmentation"
    - "hypercolumns for object segmentation and fine grained localization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "a new ensemble learning framework for 3d biomedical image segmentation"):
  with members:
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "ensembling neural networks for digital pathology images classification and segmentation"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "xgboost a scalable tree boosting system"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "dense transformer networks"):
  with members:
    - "deformable convolutional networks"
    - "stacked hourglass networks for human pose estimation"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "deeper depth prediction with fully convolutional residual networks"
    - "image to image translation with conditional adversarial networks"
    - "group equivariant convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "morphological error detection in 3d segmentations"):
  with members:
    - "toward streaming synapse detection with compositional convnets"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "deep tensor convolution on multicores"
    - "flood filling networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"):
  with members:
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "object skeleton extraction in natural images by fusing scale associated deep side outputs"
    - "holistically nested edge detection"
    - "iterative instance segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"

Connected through hyperedge HE-8 (induced by source paper "toward streaming synapse detection with compositional convnets"):
  with members:
    - "deep tensor convolution on multicores"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "a context aware delayed agglomeration framework for electron microscopy segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"
    - "imagenet large scale visual recognition challenge"
    - "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)