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 "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"):
  Abstract: In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and describe them in the radiology report. In this paper, we study the lesion description or annotation problem. Given a lesion image, our aim is to predict a comprehensive set of relevant labels, such as the lesion's body part, type, and attributes, which may assist downstream fine-grained diagnosis. To address this task, we first design a deep learning module to extract relevant semantic labels from the radiology reports associated with the lesion images. With the images and text-mined labels, we propose a lesion annotation network (LesaNet) based on a multilabel convolutional neural network (CNN) to learn all labels holistically. Hierarchical relations and mutually exclusive relations between the labels are leveraged to improve the label prediction accuracy. The relations are utilized in a label expansion strategy and a relational hard example mining algorithm. We also attach a simple score propagation layer on LesaNet to enhance recall and explore implicit relation between labels. Multilabel metric learning is combined with classification to enable interpretable prediction. We evaluated LesaNet on the public DeepLesion dataset, which contains over 32K diverse lesion images. Experiments show that LesaNet can precisely annotate the lesions using an ontology of 171 fine-grained labels with an average AUC of 0.9344.

Includes members:
  Member "a self attention based deep learning method for lesion attribute detection from ct reports":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what can computational models learn from human selective attention a review from an audiovisual crossmodal perspective")
  Member "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels")
  Member "tienet text image embedding network for common thorax disease classification and reporting in chest x rays":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised lesion segmentation on ct scans using co segmentation")
  Member "imbalanced deep learning by minority class incremental rectification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "3d context enhanced region based convolutional neural network for end to end lesion detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "biosentvec creating sentence embeddings for biomedical texts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)