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: "assessment of algorithms for mitosis detection in breast cancer histopathology images"
  Abstract: The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.#R##N##R##N#In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

Connected through hyperedge HE-1 (induced by source paper "roto translation equivariant convolutional networks application to histopathology image analysis"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "b spline cnns on lie groups"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "3d g cnns for pulmonary nodule detection"
    - "computing cnn loss and gradients for pose estimation with riemannian geometry"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

Connected through hyperedge HE-2 (induced by source paper "deep object detection based mitosis analysis in breast cancer histopathological images"):
  with members:
    - "multi stream faster rcnn for mitosis counting in breast cancer images"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "feature pyramid networks for object detection"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (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"
    - "machine learning methods for histopathological image analysis"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep learning models for digital pathology"):
  with members:
    - "two stage convolutional neural network for breast cancer histology image classification"
    - "inferring a third spatial dimension from 2d histological images"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "context based normalization of histological stains using deep convolutional features"
    - "context aware stacked convolutional neural networks for classification of breast carcinomas in whole slide histopathology images"
    - "representation aggregation networks for segmentation of multi gigapixel histology images"
    - "detecting cancer metastases on gigapixel pathology images"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"

Connected through hyperedge HE-5 (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 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"
    - "gland instance segmentation using deep multichannel neural networks"
    - "a survey on deep learning in medical image analysis"
    - "rethinking the inception architecture for computer vision"
    - "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 "field of interest prediction for computer aided mitotic count"):
  with members:
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "sliderunner a tool for massive cell annotations in whole slide images"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "predicting breast tumor proliferation from whole slide images the tupac16 challenge"):
  with members:
    - "is the winner really the best a critical analysis of common research practice in biomedical image analysis competitions"
    - "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale 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)