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: "representation aggregation networks for segmentation of multi gigapixel histology images"
  Abstract: Convolutional Neural Network (CNN) models have become the state-of-the-art for most computer vision tasks with natural images. However, these are not best suited for multi-gigapixel resolution Whole Slide Images (WSIs) of histology slides due to large size of these images. Current approaches construct smaller patches from WSIs which results in the loss of contextual information. We propose to capture the spatial context using novel Representation-Aggregation Network (RAN) for segmentation purposes, wherein the first network learns patch-level representation and the second network aggregates context from a grid of neighbouring patches. We can use any CNN for representation learning, and can utilize CNN or 2D-Long Short Term Memory (2D-LSTM) for context-aggregation. Our method significantly outperformed conventional patch-based CNN approaches on segmentation of tumour in WSIs of breast cancer tissue sections.

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"
    - "detecting cancer metastases on gigapixel pathology images"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "methods for segmentation and classification of digital microscopy tissue images"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "context aware learning using transferable features for classification of breast cancer histology images"):
  with members:
    - "context aware stacked convolutional neural networks for classification of breast carcinomas in whole slide histopathology images"
    - "deep learning for classification of colorectal polyps on whole slide images"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)