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: "deep learning assessment of tumor proliferation in breast cancer histological images"
  Abstract: Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction. It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression. Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.

Connected through hyperedge HE-1 (induced by source paper "an end to end deep learning histochemical scoring system for breast cancer tma"):
  with members:
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep label distribution learning with label ambiguity"
    - "detecting cancer metastases on gigapixel pathology images"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "reversed active learning based atrous densenet for pathological image classification"):
  with members:
    - "an end to end deep learning histochemical scoring system for breast cancer tma"
    - "detecting cancer metastases on gigapixel pathology images"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "partial labeled gastric tumor segmentation via patch based reiterative learning"
    - "wide residual networks"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "patch based convolutional neural network for whole slide tissue image classification"

Connected through hyperedge HE-4 (induced by source paper "triresnet a deep triple stream residual network for histopathology grading"):
  with members:
    - "do better imagenet models transfer better"
    - "deep learning framework for multi class breast cancer histology image classification"
    - "cancer metastasis detection with neural conditional random field"
    - "detecting cancer metastases on gigapixel pathology images"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

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)