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: "icadx interpretable computer aided diagnosis of breast masses"
  Abstract: In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

Connected through hyperedge HE-1 (induced by source paper "iterative augmentation of visual evidence for weakly supervised lesion localization in deep interpretability frameworks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deepseenet a deep learning model for automated classification of patient based age related macular degeneration severity from color fundus photographs"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "generative image inpainting with contextual attention"
    - "deep image mining for diabetic retinopathy screening"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "generation of multimodal justification using visual word constraint model for explainable computer aided diagnosis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "brain tumor segmentation with deep neural networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "skeleton key image captioning by skeleton attribute decomposition"

Connected through hyperedge HE-3 (induced by source paper "parallel medical imaging a new data knowledge driven evolutionary framework for medical image analysis"):
  with members:
    - "melanogans high resolution skin lesion synthesis with gans"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "gans for medical image analysis"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "what do we need to build explainable ai systems for the medical domain"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "retinal optic disc segmentation using conditional generative adversarial network"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "conditional generative adversarial and convolutional networks for x ray breast mass segmentation and shape classification"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "a survey on deep learning in medical image analysis"
    - "deep learning with topological signatures"
    - "ssd single shot multibox detector"

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)