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: "thoracic disease identification and localization with limited supervision"
  Abstract: Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate prediction model for these tasks usually requires a large number of images manually annotated with labels and finding sites of abnormalities. In reality, however, such annotated data are expensive to acquire, especially the ones with location annotations. We need methods that can work well with only a small amount of location annotations. To address this challenge, we present a unified approach that simultaneously performs disease identification and localization through the same underlying model for all images. We demonstrate that our approach can effectively leverage both class information as well as limited location annotation, and significantly outperforms the comparative reference baseline in both classification and localization tasks.

Connected through hyperedge HE-1 (induced by source paper "generative modeling for small data object detection"):
  with members:
    - "inserting videos into videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "versatile auxiliary classifier with generative adversarial network vac gan multi class scenarios"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"

Connected through hyperedge HE-2 (induced by source paper "attention based glaucoma detection a large scale database and cnn model"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"
    - "supervising neural attention models for video captioning by human gaze data"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "addressing data bias problems for chest x ray image report generation"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"):
  with members:
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "data distillation towards omni supervised learning"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"

Connected through hyperedge HE-5 (induced by source paper "multi instance multi scale cnn for medical image classification"):
  with members:
    - "scale aware trident networks for object detection"
    - "attention based deep multiple instance learning"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "feature pyramid networks for object detection"
    - "fast r cnn"
    - "locally scale invariant convolutional neural networks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "learning interpretable features via adversarially robust optimization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "learning interpretable anatomical features through deep generative models application to cardiac remodeling"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "learning deep features for discriminative localization"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

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)