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: "the importance of stain normalization in colorectal tissue classification with convolutional networks"
  Abstract: The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H&E images.

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"
    - "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"
    - "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 "a closer look at domain shift for deep learning in histopathology"):
  with members:
    - "quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology"
    - "transfusion understanding transfer learning for medical imaging"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep learning in medical image analysis and multimodal learning for clinical decision support"
    - "adversarial examples in the physical world"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-3 (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"
    - "gland instance segmentation using deep multichannel neural networks"
    - "a survey on deep learning in medical image analysis"
    - "rethinking the inception architecture for computer vision"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "very deep convolutional networks for large scale image recognition"
    - "mahotas open source software for scriptable computer vision"

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"
    - "classification and retrieval of digital pathology scans a new dataset"
    - "a survey on deep learning in medical image analysis"
    - "neural stain style transfer learning using gan for histopathological images"
    - "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 "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"
    - "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"
    - "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-7 (induced by source paper "deep learning framework for multi class breast cancer histology image classification"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "detecting cancer metastases on gigapixel pathology images"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "dual path networks"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "classification and disease localization in histopathology using only global labels a weakly supervised approach"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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)