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: "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
  Abstract: Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.

Connected through hyperedge HE-1 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "medical image analysis using convolutional neural networks a review"
    - "skin lesion segmentation u nets versus clustering"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "the importance of skip connections in biomedical image segmentation"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"

Connected through hyperedge HE-2 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks"):
  with members:
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "towards neural networks that provably know when they don t know"
    - "correlation via synthesis end to end nodule image generation and radiogenomic map learning based on generative adversarial network"
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "generative adversarial network in medical imaging a review"
    - "batch normalization is a cause of adversarial vulnerability"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"

Connected through hyperedge HE-3 (induced by source paper "automatic pulmonary nodule detection in ct scans using convolutional neural networks based on maximum intensity projection"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "coronary artery segmentation from intravascular optical coherence tomography using deep capsules"):
  with members:
    - "automatic plaque detection in ivoct pullbacks using convolutional neural networks"
    - "dynamic routing between capsules"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (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-7 (induced by source paper "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "a three player gan generating hard samples to improve classification networks"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "red blood cell image generation for data augmentation using conditional generative adversarial networks"
    - "towards generative adversarial networks as a new paradigm for radiology education"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"

Connected through hyperedge HE-8 (induced by source paper "needles in haystacks on classifying tiny objects in large images"):
  with members:
    - "pathologist level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "r 2 cnn fast tiny object detection in large scale remote sensing images"
    - "processing megapixel images with deep attention sampling models"
    - "attention based deep multiple instance learning"
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "exploring the limits of weakly supervised pretraining"
    - "3d multirater rcnn for multimodal multiclass detection and characterisation of extremely small objects"

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)