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 "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"):
  Abstract: When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report. To automate this process, we propose a multitask universal lesion analysis network (MULAN) for joint detection, tagging, and segmentation of lesions in a variety of body parts, which greatly extends existing work of single-task lesion analysis on specific body parts. MULAN is based on an improved Mask R-CNN framework with three head branches and a 3D feature fusion strategy. It achieves the state-of-the-art accuracy in the detection and tagging tasks on the DeepLesion dataset, which contains 32K lesions in the whole body. We also analyze the relationship between the three tasks and show that tag predictions can improve detection accuracy via a score refinement layer.

Includes members:
  Member "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automated enriched medical concept generation for chest x ray images")
  Member "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d aggregated faster r cnn for general lesion detection")
  Member "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised lesion segmentation on ct scans using co segmentation")
  Member "joint learning for pulmonary nodule segmentation attributes and malignancy prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping")
  Member "3d context enhanced region based convolutional neural network for end to end lesion detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "semi automatic recist labeling on ct scans with cascaded convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised lesion segmentation on ct scans using co segmentation")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "detecting and classifying lesions in mammograms with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")

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)