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: "gland instance segmentation by deep multichannel side supervision"
  Abstract: In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional and boundary patterns, with side supervision (deep supervision on side responses) in gland histology images. Our proposed system, deep multichannel side supervision (DMCS), alleviates heavy feature design due to the use of convolutional neural networks guided by side supervision. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge, we observe state-of-the-art results based on a number of evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "adaptive weighting multi field of view cnn for semantic segmentation in pathology"):
  with members:
    - "autofocus layer for semantic segmentation"
    - "improving whole slide segmentation through visual context a systematic study"
    - "switching convolutional neural network for crowd counting"
    - "squeeze and excitation networks"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "learning convolutional networks for content weighted image compression"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "dynamic routing between capsules"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "gland instance segmentation using deep multichannel neural networks"
    - "a survey on deep learning in medical image analysis"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland segmentation in colon histology images the glas challenge contest"

Connected through hyperedge HE-3 (induced by source paper "attentionboost learning what to attend by boosting fully convolutional networks"):
  with members:
    - "image segmentation by iterative inference from conditional score estimation"
    - "focal loss for dense object detection"
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "gland instance segmentation using deep multichannel neural networks"
    - "a survey on deep learning in medical image analysis"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "efforts estimation of doctors annotating medical image"):
  with members:
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "decoupled deep neural network for semi supervised semantic segmentation"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "micro net a unified model for segmentation of various objects in microscopy images"):
  with members:
    - "gland instance segmentation using deep multichannel neural networks"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "quantization of fully convolutional networks for accurate biomedical image segmentation"):
  with members:
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "gland instance segmentation using deep multichannel neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-8 (induced by source paper "gland instance segmentation using deep multichannel neural networks"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "object detection free instance segmentation with labeling transformations"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "learning to segment object candidates"
    - "holistically nested edge detection"

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)