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: "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
  Abstract: Abstract   Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs.  The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI.  The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth.  This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task.

Connected through hyperedge HE-1 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "processing megapixel images with deep attention sampling models"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "neural image compression for gigapixel histopathology image analysis"
    - "attention based deep multiple instance learning"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "smoothgrad removing noise by adding noise"

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"
    - "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"
    - "machine learning methods for histopathological image analysis"
    - "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 "deep learning for whole slide image analysis an overview"):
  with members:
    - "learning where to see a novel attention model for automated immunohistochemical scoring"
    - "neural image compression for gigapixel histopathology image analysis"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "attention based deep multiple instance learning"
    - "classification and disease localization in histopathology using only global labels a weakly supervised approach"
    - "cancer metastasis detection with neural conditional random field"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-4 (induced by source paper "field of interest prediction for computer aided mitotic count"):
  with members:
    - "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"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "examining the capability of gans to replace real biomedical images in classification models training"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "gans for medical image analysis"
    - "progressive growing of gans for improved quality stability and variation"
    - "a survey on deep learning in medical image analysis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "influence of control parameters and the size of biomedical image datasets on the success of adversarial attacks"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "a survey on deep learning in medical image analysis"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "neural image compression for gigapixel histopathology image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based deep multiple instance learning"
    - "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"
    - "representation learning with contrastive predictive coding"
    - "comparison of different methods for tissue segmentation in histopathological whole slide images"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"

Connected through hyperedge HE-8 (induced by source paper "training convolutional neural networks and compressed sensing end to end for microscopy cell detection"):
  with members:
    - "a practical study of longitudinal reference based compressed sensing for mri"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "multi label prediction via compressed sensing"
    - "solving multiclass learning problems via error correcting output codes"

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)