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: "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
  Abstract: Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.

Connected through hyperedge HE-1 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "frage frequency agnostic word representation"
    - "retina u net embarrassingly simple exploitation of segmentation supervision for medical object detection"
    - "automated soft tissue lesion detection and segmentation in digital mammography using a u net deep learning network"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"
    - "darts differentiable architecture search"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-2 (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-3 (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-4 (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"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "3d aggregated faster r cnn for general lesion detection"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "mini lesions detection on diabetic retinopathy images via large scale cnn features"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "applications of deep learning and reinforcement learning to biological data"
    - "towards single phase single stage detection of pulmonary nodules in chest ct imaging"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards universal object detection by domain attention"):
  with members:
    - "yolov3 an incremental improvement"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"):
  with members:
    - "a self attention based deep learning method for lesion attribute detection from ct reports"
    - "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"
    - "imbalanced deep learning by minority class incremental rectification"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (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"

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)