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: "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
  Abstract: Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer and max-min pooling for classifying common thoracic diseases as well as localizing suspicious lesion regions on chest X-rays. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of proposed model and its better performance against the state-of-the-art pipelines.

Connected through hyperedge HE-1 (induced by source paper "when labelled data hurts deep semi supervised classification with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "label propagation for deep semi supervised learning"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "smooth neighbors on teacher graphs for semi supervised learning"
    - "improved techniques for training gans"

Connected through hyperedge HE-2 (induced by source paper "the graphnet zoo a plug and play framework for deep semi supervised classification"):
  with members:
    - "two cycle learning clustering based regularisation for deep semi supervised classification"
    - "graphx net chest x ray classification under extreme minimal supervision"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "semantic transferable weakly supervised endoscopic lesions segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-4 (induced by source paper "fibinet combining feature importance and bilinear feature interaction for click through rate prediction"):
  with members:
    - "skin lesion classification with ensemble of squeeze and excitation networks and semi supervised learning"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "learning what and where to attend"
    - "recalibrating fully convolutional networks with spatial and channel squeeze excitation blocks"
    - "neural factorization machines for sparse predictive analytics"
    - "field aware factorization machines in a real world online advertising system"
    - "deep cross network for ad click predictions"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "xnet a convolutional neural network cnn implementation for medical x ray image segmentation suitable for small datasets"):
  with members:
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "on calibration of modern neural networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "infomask masked variational latent representation to localize chest disease"):
  with members:
    - "classifier agnostic saliency map extraction"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "smoothgrad removing noise by adding noise"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "deep variational information bottleneck"

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)