human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clinically accurate chest x ray report generation")
  Member "learning to diagnose from scratch by exploiting dependencies among labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels")
  Member "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "learning to recognize abnormalities in chest x rays with location aware dense networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vulnerability analysis of chest x ray image classification against adversarial attacks")
  Member "deep learning with lung segmentation and bone shadow exclusion techniques for chest x ray analysis of lung cancer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks")
  Member "weakly supervised medical diagnosis and localization from multiple resolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "chexnet radiologist level pneumonia detection on chest x rays with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "thoracic disease identification and localization with limited supervision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative modeling for small data object detection")

Question: Please tell me which class the source paper 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)