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: "machine learning methods for histopathological image analysis"
  Abstract: Abstract   Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

Connected through hyperedge HE-1 (induced by source paper "multi stream faster rcnn for mitosis counting in breast cancer images"):
  with members:
    - "bilinear faster rcnn with ela for image tampering detection"
    - "learning rich features for image manipulation detection"
    - "mitos rcnn a novel approach to mitotic figure detection in breast cancer histopathology images using region based convolutional neural networks"
    - "cell detection with deep convolutional neural network and compressed sensing"
    - "u net convolutional networks for biomedical image segmentation"
    - "compact bilinear pooling"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"

Connected through hyperedge HE-2 (induced by source paper "quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology"):
  with members:
    - "epithelium segmentation using deep learning in h e stained prostate specimens with immunohistochemistry as reference standard"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"
    - "context based normalization of histological stains using deep convolutional features"
    - "detecting cancer metastases on gigapixel pathology images"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "neural stain style transfer learning using gan for histopathological images"
    - "gland segmentation in colon histology images the glas challenge contest"

Connected through hyperedge HE-3 (induced by source paper "histopathologic image processing a review"):
  with members:
    - "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"
    - "gland instance segmentation using deep multichannel neural 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-4 (induced by source paper "automated classification of histopathology images using transfer learning"):
  with members:
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "classification and retrieval of digital pathology scans a new dataset"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "highly efficient follicular segmentation in thyroid cytopathological whole slide image"):
  with members:
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised shape and motion analysis of 3822 cardiac 4d mris of uk biobank"):
  with members:
    - "unsupervised segmentation of 3d medical images based on clustering and deep representation learning"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "3d consistent biventricular myocardial segmentation using deep learning for mesh generation"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-7 (induced by source paper "convolutional neural networks for multi class histopathology image classification"):
  with members:
    - "classification and retrieval of digital pathology scans a new dataset"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "deep semi supervised generative learning for automated tumor proportion scoring on nsclc tissue needle biopsies"):
  with members:
    - "staingan stain style transfer for digital histological images"
    - "spectral normalization for generative adversarial networks"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "neural stain style transfer learning using gan for histopathological images"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)