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: "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
  Abstract: Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. intensity and texture) between lesions and other tissues, making it especially difficult to develop a universal lesion detector. Instead of developing a specific-type lesion detector, this work builds a Universal Lesion Detector (ULDor) based on Mask R-CNN, which is able to detect all different kinds of lesions from whole body parts. As a state-of-the-art object detector, Mask R-CNN adds a branch for predicting segmentation masks on each Region of Interest (RoI) to improve the detection performance. However, it is almost impossible to manually annotate a large-scale dataset with pixel-level lesion masks to train the Mask R-CNN for lesion detection. To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection. On the other hand, this work proposes a hard negative example mining strategy to reduce the false positives for improving the detection performance. Experimental results on the NIH DeepLesion dataset demonstrate that the ULDor is enhanced using pseudo masks and the proposed hard negative example mining strategy and achieves a sensitivity of 86.21% with five false positives per image.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised lesion segmentation on ct scans using co segmentation"):
  with members:
    - "weakly supervised lesion co segmentation on ct scans"
    - "xlsor a robust and accurate lung segmentor on chest x rays using criss cross attention and customized radiorealistic abnormalities generation"
    - "abnormal chest x ray identification with generative adversarial one class classifier"
    - "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "deep object co segmentation"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised lesion co segmentation on ct scans"):
  with members:
    - "weakly supervised lesion segmentation on ct scans using co segmentation"
    - "xlsor a robust and accurate lung segmentor on chest x rays using criss cross attention and customized radiorealistic abnormalities generation"
    - "abnormal chest x ray identification with generative adversarial one class classifier"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"

Connected through hyperedge HE-3 (induced by source paper "lesion harvester iteratively mining unlabeled lesions and hard negative examples at scale"):
  with members:
    - "attentive ct lesion detection using deep pyramid inference with multi scale booster"
    - "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"
    - "objects as points"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "soft sampling for robust object detection"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "focal loss for dense object detection"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-4 (induced by source paper "xlsor a robust and accurate lung segmentor on chest x rays using criss cross attention and customized radiorealistic abnormalities generation"):
  with members:
    - "fine grained lesion annotation in ct images with knowledge mined from radiology reports"
    - "abnormal chest x ray identification with generative adversarial one class classifier"
    - "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement"
    - "multimodal unsupervised image to image translation"
    - "ccnet criss cross attention for semantic segmentation"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"

Connected through hyperedge HE-5 (induced by source paper "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"):
  with members:
    - "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "semi automatic recist labeling on ct scans with cascaded convolutional neural networks"
    - "mask r cnn"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "mvp net multi view fpn with position aware attention for deep universal lesion detection"):
  with members:
    - "liver lesion detection from weakly labeled multi phase ct volumes with a grouped single shot multibox detector"
    - "cbam convolutional block attention module"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "unsupervised body part regression via spatially self ordering convolutional neural networks"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "r fcn object detection via region based fully convolutional networks"

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)