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: "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
  Abstract: Predicting TNM stage is the major determinant of breast cancer prognosis and treatment. The essential part of TNM stage classification is whether the cancer has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage (pN-stage) is commonly performed by pathologists detecting metastasis in histological slides. However, this diagnostic procedure is prone to misinterpretation and would normally require extensive time by pathologists because of the sheer volume of data that needs a thorough review. Automated detection of lymph node metastasis and pN-stage prediction has a great potential to reduce their workload and help the pathologist. Recent advances in convolutional neural networks (CNN) have shown significant improvements in histological slide analysis, but accuracy is not optimized because of the difficulty in the handling of gigapixel images. In this paper, we propose a robust method for metastasis detection and pN-stage classification in breast cancer from multiple gigapixel pathology images in an effective way. pN-stage is predicted by combining patch-level CNN based metastasis detector and slide-level lymph node classifier. The proposed framework achieves a state-of-the-art quadratic weighted kappa score of 0.9203 on the Camelyon17 dataset, outperforming the previous winning method of the Camelyon17 challenge.

Connected through hyperedge HE-1 (induced by source paper "needles in haystacks on classifying tiny objects in large images"):
  with members:
    - "pathologist level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "r 2 cnn fast tiny object detection in large scale remote sensing images"
    - "processing megapixel images with deep attention sampling models"
    - "attention based deep multiple instance learning"
    - "exploring the limits of weakly supervised pretraining"
    - "3d multirater rcnn for multimodal multiclass detection and characterisation of extremely small objects"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-2 (induced by source paper "learning loss for active learning"):
  with members:
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "active self paced learning for cost effective and progressive face identification"
    - "exploring the limits of weakly supervised pretraining"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "representation learning by learning to count"
    - "deep bayesian active learning with image data"
    - "stacked hourglass networks for human pose estimation"
    - "active and continuous exploration with deep neural networks and expected model output changes"

Connected through hyperedge HE-3 (induced by source paper "self supervised similarity learning for digital pathology"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "deep learning is robust to massive label noise"
    - "unsupervised representation learning by predicting image rotations"
    - "virtualization of tissue staining in digital pathology using an unsupervised deep learning approach"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "cell detection in microscopy images with deep convolutional neural network and compressed sensing"
    - "colorful image colorization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "pfa scannet pyramidal feature aggregation with synergistic learning for breast cancer metastasis analysis"):
  with members:
    - "cia net robust nuclei instance segmentation with contour aware information aggregation"
    - "cancer metastasis detection with neural conditional random field"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "detecting cancer metastases on gigapixel pathology images"
    - "feature pyramid networks for object detection"

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)