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: "3d context enhanced region based convolutional neural network for end to end lesion detection"
  Abstract: Detecting lesions from computed tomography (CT) scans is an important but difficult problem because non-lesions and true lesions can appear similar. 3D context is known to be helpful in this differentiation task. However, existing end-to-end detection frameworks of convolutional neural networks (CNNs) are mostly designed for 2D images. In this paper, we propose 3D context enhanced region-based CNN (3DCE) to incorporate 3D context information efficiently by aggregating feature maps of 2D images. 3DCE is easy to train and end-to-end in training and inference. A universal lesion detector is developed to detect all kinds of lesions in one algorithm using the DeepLesion dataset. Experimental results on this challenging task prove the effectiveness of 3DCE. We have released the code of 3DCE in this https URL

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 "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"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "non local neural networks"
    - "attention is all you need"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-3 (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"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "focal loss for dense object detection"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-4 (induced by source paper "detecting lesion bounding ellipses with gaussian proposal networks"):
  with members:
    - "bounding box regression with uncertainty for accurate object detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "towards the success rate of one real time unconstrained salient object detection"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "mask r cnn"
    - "learning a rotation invariant detector with rotatable bounding box"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "attentive ct lesion detection using deep pyramid inference with multi scale booster"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "squeeze and excitation networks"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "fast preprocessing for robust face sketch synthesis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "mvp net multi view fpn with position aware attention for deep universal lesion detection"):
  with members:
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "liver lesion detection from weakly labeled multi phase ct volumes with a grouped single shot multibox detector"
    - "cbam convolutional block attention module"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "improving deep lesion detection using 3d contextual and spatial attention"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "cbam convolutional block attention module"
    - "thoracic disease identification and localization with limited supervision"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "residual attention network for image classification"
    - "r fcn object detection via region based fully convolutional networks"
    - "faster r cnn towards real time object detection with region proposal 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)