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: "neural stain style transfer learning using gan for histopathological images"
  Abstract: Performance of data-driven network for tumor classification varies with stain-style of histopathological images. This article proposes the stain-style transfer (SST) model based on conditional generative adversarial networks (GANs) which is to learn not only the certain color distribution but also the corresponding histopathological pattern. Our model considers feature-preserving loss in addition to well-known GAN loss. Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images. The model is examined using the CAMELYON16 dataset.

Connected through hyperedge HE-1 (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-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"
    - "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"
    - "gland segmentation in colon histology images the glas challenge contest"

Connected through hyperedge HE-3 (induced by source paper "fast gpu enabled color normalization for digital pathology"):
  with members:
    - "staingan stain style transfer for digital histological images"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "quantitative phase imaging and artificial intelligence a review"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "choicenet robust learning by revealing output correlations"
    - "deep learning enhanced mobile phone microscopy"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-5 (induced by source paper "machine learning methods for histopathological image analysis"):
  with members:
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "detecting cancer metastases on gigapixel pathology images"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "classification and retrieval of digital pathology scans a new dataset"
    - "a survey on deep learning in medical image analysis"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "automatic breast cancer grading in lymph nodes using a deep neural network"

Connected through hyperedge HE-6 (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"
    - "machine learning methods for histopathological image analysis"
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "computational histological staining and destaining of prostate core biopsy rgb images with generative adversarial neural networks"):
  with members:
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "image to image translation with conditional adversarial networks"

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)