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: "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
  Abstract: In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursive-input fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.

Connected through hyperedge HE-1 (induced by source paper "boundary aware feature propagation for scene segmentation"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "feature boosting network for 3d pose estimation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "the mutex watershed and its objective efficient parameter free image partitioning"):
  with members:
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "learning steerable filters for rotation equivariant cnns"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-3 (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"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "gland instance segmentation by deep multichannel side supervision"

Connected through hyperedge HE-4 (induced by source paper "ace net biomedical image segmentation with augmented contracting and expansive paths"):
  with members:
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "learning a discriminative feature network for semantic segmentation"
    - "learning steerable filters for rotation equivariant cnns"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "the importance of skip connections in biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "deep predictive coding network for object recognition"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "learning long range spatial dependencies with horizontal gated recurrent units"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "can recurrent neural networks warp time"
    - "learning what and where to attend"

Connected through hyperedge HE-6 (induced by source paper "deep smoke segmentation"):
  with members:
    - "a cnn rnn architecture for multi label weather recognition"
    - "deeply supervised salient object detection with short connections"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "video salient object detection via fully convolutional networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "lstm fully convolutional networks for time series classification"
    - "going deeper with contextual cnn for hyperspectral image classification"

Connected through hyperedge HE-7 (induced by source paper "semi supervised multi organ segmentation via deep multi planar co training"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "deep co training for semi supervised image recognition"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "data distillation towards omni supervised learning"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"

Connected through hyperedge HE-8 (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"
    - "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"
    - "densely connected convolutional networks"

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)