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: "identifying metastases in sentinel lymph nodes with deep convolutional neural networks"
  Abstract: Metastatic presence in lymph nodes is one of the most important prognostic variables of breast cancer. The current diagnostic procedure for manually reviewing sentinel lymph nodes, however, is very time-consuming and subjective. Pathologists have to manually scan an entire digital whole-slide image (WSI) for regions of metastasis that are sometimes only detectable under high resolution or entirely hidden from the human visual cortex. From October 2015 to April 2016, the International Symposium on Biomedical Imaging (ISBI) held the Camelyon Grand Challenge 2016 to crowd-source ideas and algorithms for automatic detection of lymph node metastasis. Using a generalizable stain normalization technique and the Proscia Pathology Cloud computing platform, we trained a deep convolutional neural network on millions of tissue and tumor image tiles to perform slide-based evaluation on our testing set of whole-slide images images, with a sensitivity of 0.96, specificity of 0.89, and AUC score of 0.90. Our results indicate that our platform can automatically scan any WSI for metastatic regions without institutional calibration to respective stain profiles.

Connected through hyperedge HE-1 (induced by source paper "computer aided detection of squamous carcinoma of the cervix in whole slide images"):
  with members:
    - "deep learning framework for multi class breast cancer histology image classification"
    - "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"
    - "patch based convolutional neural network for whole slide tissue image classification"

Connected through hyperedge HE-2 (induced by source paper "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"):
  with members:
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "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"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "automatic breast cancer grading in lymph nodes using a deep neural network"):
  with members:
    - "detecting cancer metastases on gigapixel pathology images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "densely connected convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "network in network"

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)