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: "comparison of deep learning approaches for multi label chest x ray classification"
  Abstract: The increased availability of X-ray image archives (e.g. the ChestX-ray14 dataset from the NIH Clinical Center) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classification, we investigate a powerful network architecture in detail: the ResNet-50. Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-ray network from scratch. To leverage the high spatial resolutions of X-ray data, we also include an extended ResNet-50 architecture, and a network integrating non-image data (patient age, gender and acquisition type) in the classification process. In a systematic evaluation, using 5-fold re-sampling and a multi-label loss function, we evaluate the performance of the different approaches for pathology classification by ROC statistics and analyze differences between the classifiers using rank correlation. We observe a considerable spread in the achieved performance and conclude that the X-ray-specific ResNet-50, integrating non-image data yields the best overall results.

Connected through hyperedge HE-1 (induced by source paper "when labelled data hurts deep semi supervised classification with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "label propagation for deep semi supervised learning"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "smooth neighbors on teacher graphs for semi supervised learning"
    - "improved techniques for training gans"

Connected through hyperedge HE-2 (induced by source paper "the graphnet zoo a plug and play framework for deep semi supervised classification"):
  with members:
    - "two cycle learning clustering based regularisation for deep semi supervised classification"
    - "graphx net chest x ray classification under extreme minimal supervision"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "vulnerability analysis of chest x ray image classification against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attacks against medical deep learning systems"
    - "adversarial logit pairing"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "learning to recognize abnormalities in chest x rays with location aware dense networks"
    - "learning transferable architectures for scalable image recognition"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"):
  with members:
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "deep learning with lung segmentation and bone shadow exclusion techniques for chest x ray analysis of lung cancer"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"

Connected through hyperedge HE-5 (induced by source paper "when does bone suppression and lung field segmentation improve chest x ray disease classification"):
  with members:
    - "iterative attention mining for weakly supervised thoracic disease pattern localization in chest x rays"
    - "deep learning with lung segmentation and bone shadow exclusion techniques for chest x ray analysis of lung cancer"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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)