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 framework for multi class breast cancer histology image classification"
  Abstract: In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH). As these histology images are too large to fit into GPU memory, we first propose using Inception V3 to perform patch level classification. The patch level predictions are then passed through an ensemble fusion framework involving majority voting, gradient boosting machine (GBM), and logistic regression to obtain the image level prediction. We improve the sensitivity of the Normal and Benign predicted classes by designing a Dual Path Network (DPN) to be used as a feature extractor where these extracted features are further sent to a second layer of ensemble prediction fusion using GBM, logistic regression, and support vector machine (SVM) to refine predictions. Experimental results demonstrate our framework shows a 12.5$\%$ improvement over the state-of-the-art model.

Connected through hyperedge HE-1 (induced by source paper "computer aided detection of squamous carcinoma of the cervix in whole slide images"):
  with members:
    - "cancer metastasis detection with neural conditional random field"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "recent advances in the applications of convolutional neural networks to medical image contour detection"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "deep retinal image understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identifying metastases in sentinel lymph nodes with deep convolutional neural networks"
    - "patch based convolutional neural network for whole slide tissue image classification"

Connected through hyperedge HE-2 (induced by source paper "cancer metastasis detection with neural conditional random field"):
  with members:
    - "detecting cancer metastases on gigapixel pathology images"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "triresnet a deep triple stream residual network for histopathology grading"):
  with members:
    - "do better imagenet models transfer better"
    - "cancer metastasis detection with neural conditional random field"
    - "detecting cancer metastases on gigapixel pathology images"
    - "deep learning assessment of tumor proliferation in breast cancer histological 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)