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: "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"
  Abstract: Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect mitotic figures in breast cancer tissue sections based on convolutional neural networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by noisy and expensive reference standards established by pathologists, lack of generalization due to staining variation across laboratories, and high computational requirements needed to process gigapixel whole-slide images (WSIs). In this paper, we present a method to train and evaluate CNNs to specifically solve these issues in the context of mitosis detection in breast cancer WSIs. First, by combining image analysis of mitotic activity in phosphohistone-H3 restained slides and registration, we built a reference standard for mitosis detection in entire H&E WSIs requiring minimal manual annotation effort. Second, we designed a data augmentation strategy that creates diverse and realistic H&E stain variations by modifying H&E color channels directly. Using it during training combined with network ensembling resulted in a stain invariant mitosis detector. Third, we applied knowledge distillation to reduce the computational requirements of the mitosis detection ensemble with a negligible loss of performance. The system was trained in a single-center cohort and evaluated in an independent multicenter cohort from the cancer genome atlas on the three tasks of the tumor proliferation assessment challenge. We obtained a performance within the top three best methods for most of the tasks of the challenge.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "u net convolutional networks for biomedical image segmentation"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "a semi supervised framework for automatic pixel wise breast cancer grading of histological images"):
  with members:
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"

Connected through hyperedge HE-4 (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"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "attention based deep multiple instance learning"
    - "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-5 (induced by source paper "leveraging unlabeled whole slide images for mitosis detection"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "data distillation towards omni supervised learning"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "deep residual learning for image recognition"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "strategies for training stain invariant cnns"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "u net convolutional networks for biomedical image segmentation"

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)