human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology"):
  Abstract: Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invariant CNNs. The latter aims to match training and test color distributions in order to reduce stain variation. For the first time, we compared some of these techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories. Additionally, we propose a novel unsupervised method to perform stain color normalization using a neural network. Based on our experimental results, we provide practical guidelines on how to use stain color augmentation and stain color normalization in future computational pathology applications.

Includes members:
  Member "epithelium segmentation using deep learning in h e stained prostate specimens with immunohistochemistry as reference standard":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advanced deep convolutional neural network approaches for digital pathology image analysis a comprehensive evaluation with different use cases")
  Member "predicting breast tumor proliferation from whole slide images the tupac16 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images")
  Member "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting breast tumor proliferation from whole slide images the tupac16 challenge")
  Member "machine learning methods for histopathological image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi stream faster rcnn for mitosis counting in breast cancer images")
  Member "context based normalization of histological stains using deep convolutional features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning models for digital pathology")
  Member "detecting cancer metastases on gigapixel pathology images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai")
  Member "the importance of stain normalization in colorectal tissue classification with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a closer look at domain shift for deep learning in histopathology")
  Member "neural stain style transfer learning using gan for histopathological images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning models for digital pathology")

Question: Please tell me which class the source paper 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)