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: "semi automatic recist labeling on ct scans with cascaded convolutional neural networks"
  Abstract: Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients. As such, RECIST annotations must be accurate. However, RECIST annotations manually labeled by radiologists require professional knowledge and are time-consuming, subjective, and prone to inconsistency among different observers. To alleviate these problems, we propose a cascaded convolutional neural network based method to semi-automatically label RECIST annotations and drastically reduce annotation time. The proposed method consists of two stages: lesion region normalization and RECIST estimation. We employ the spatial transformer network (STN) for lesion region normalization, where a localization network is designed to predict the lesion region and the transformation parameters with a multi-task learning strategy. For RECIST estimation, we adapt the stacked hourglass network (SHN), introducing a relationship constraint loss to improve the estimation precision. STN and SHN can both be learned in an end-to-end fashion. We train our system on the DeepLesion dataset, obtaining a consensus model trained on RECIST annotations performed by multiple radiologists over a multi-year period. Importantly, when judged against the inter-reader variability of two additional radiologist raters, our system performs more stably and with less variability, suggesting that RECIST annotations can be reliably obtained with reduced labor and time.

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"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "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-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"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "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"

Connected through hyperedge HE-3 (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"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "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"

Connected through hyperedge HE-4 (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"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "mask r cnn"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"):
  with members:
    - "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"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "mask r cnn"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement"):
  with members:
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"

Connected through hyperedge HE-7 (induced by source paper "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"):
  with members:
    - "accurate weakly supervised deep lesion segmentation on ct scans self paced 3d mask generation from recist"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"):
  with members:
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "weakly supervised object localization using things and stuff transfer"
    - "weakly supervised object localization using size estimates"
    - "deep residual learning for image recognition"

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)