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 "clinically accurate chest x ray report generation"):
  Abstract: The automatic generation of radiology reports given medical radiographs has significant potential to operationally and clinically improve patient care. A number of prior works have focused on this problem, employing advanced methods from computer vision and natural language generation to produce readable reports. However, these works often fail to account for the particular nuances of the radiology domain, and, in particular, the critical importance of clinical accuracy in the resulting generated reports. In this work, we present a domain-aware automatic chest X-Ray radiology report generation system which first predicts what topics will be discussed in the report, then conditionally generates sentences corresponding to these topics. The resulting system is fine-tuned using reinforcement learning, considering both readability and clinical accuracy, as assessed by the proposed Clinically Coherent Reward. We verify this system on two datasets, Open-I and MIMIC-CXR, and demonstrate that our model offers marked improvements on both language generation metrics and CheXpert assessed accuracy over a variety of competitive baselines.

Includes members:
  Member "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an adversarial approach for the robust classification of pneumonia from chest radiographs")
  Member "mimic cxr jpg a large publicly available database of labeled chest radiographs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an adversarial approach for the robust classification of pneumonia from chest radiographs")
  Member "on the automatic generation of medical imaging reports":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task learning for chest x ray abnormality classification on noisy labels")
  Member "producing radiologist quality reports for interpretable artificial intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clara clinical report auto completion")
  Member "tienet text image embedding network for common thorax disease classification and reporting in chest x rays":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "learning to summarize radiology findings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "clara clinical report auto completion")
  Member "unsupervised multimodal representation learning across medical images and reports":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "representation learning for electronic health records")

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)