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: "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
  Abstract: This paper addresses the task of nuclei segmentation in high-resolution histopathological images. We propose an auto- matic end-to-end deep neural network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the- art methods. Moreover, it is efficient that one 1000X1000 image can be segmented in less than 5 seconds. This makes it possible to precisely segment the whole-slide image in acceptable time

Connected through hyperedge HE-1 (induced by source paper "gastric histopathology image segmentation using a hierarchical conditional random field"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "improved inference via deep input transfer"):
  with members:
    - "combo loss handling input and output imbalance in multi organ segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "select attend and transfer light learnable skip connections"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "chest x ray analysis of tuberculosis by deep learning with segmentation and augmentation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "deep adversarial training for multi organ nuclei segmentation in histopathology images"):
  with members:
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep learning with cinematic rendering fine tuning deep neural networks using photorealistic medical images"
    - "rethinking monocular depth estimation with adversarial training"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "spectral normalization for generative adversarial networks"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"

Connected through hyperedge HE-4 (induced by source paper "hover net simultaneous segmentation and classification of nuclei in multi tissue histology images"):
  with members:
    - "micro net a unified model for segmentation of various objects in microscopy images"
    - "cia net robust nuclei instance segmentation with contour aware information aggregation"
    - "panoptic segmentation"
    - "methods for segmentation and classification of digital microscopy tissue images"
    - "deep learning models delineates multiple nuclear phenotypes in h e stained histology sections"
    - "a survey on deep learning in medical image analysis"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"):
  with members:
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "improved inception residual convolutional neural network for object recognition"
    - "inception recurrent convolutional neural network for object recognition"
    - "deep residual learning for image recognition"
    - "computational pathology challenges and promises for tissue analysis"

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)