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 using deep multichannel neural networks"
  Abstract: Objective:  A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not only need to be segmented from a complex background, they must also be individually identified.  Methods:  We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information—regional, location, and boundary cues—in gland histology images. Our proposed algorithm, a deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirements by altering channels.  Results:  Compared with methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent instance segmentation methods, we observe state-of-the-art results based on the evaluation metrics.  Conclusion:  The proposed deep multichannel algorithm is an effective method for gland instance segmentation.  Significance:   The generalization ability of our model not only enable the algorithm to solve gland instance segmentation problems, but the channel is also alternative that can be replaced for a specific task.

Connected through hyperedge HE-1 (induced by source paper "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "semantic segmentation using adversarial networks"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "maskplus improving mask generation for instance segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "path aggregation network for instance segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "recurrent pixel embedding for instance grouping"
    - "cascade r cnn delving into high quality object detection"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "semantic instance segmentation via deep metric learning"

Connected through hyperedge HE-3 (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"
    - "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 instance segmentation by deep multichannel side supervision"
    - "gland segmentation in colon histology images the glas challenge contest"

Connected through hyperedge HE-4 (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"
    - "a survey on deep learning in medical image analysis"
    - "gland instance segmentation by deep multichannel side supervision"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (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"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "gland instance segmentation by deep multichannel side supervision"

Connected through hyperedge HE-6 (induced by source paper "histopathologic image processing a review"):
  with members:
    - "machine learning methods for histopathological image analysis"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "rethinking the inception architecture for computer vision"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "very deep convolutional networks for large scale image recognition"
    - "mahotas open source software for scriptable computer vision"

Connected through hyperedge HE-7 (induced by source paper "fractional wavelet scattering network and applications"):
  with members:
    - "global minimum for a finsler elastica minimal path approach"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-8 (induced by source paper "micro net a unified model for segmentation of various objects in microscopy images"):
  with members:
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "gland instance segmentation by deep multichannel side supervision"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for 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)