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: "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
  Abstract: In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.

Connected through hyperedge HE-1 (induced by source paper "justifying diagnosis decisions by deep neural networks"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "generating continuous representations of medical texts"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

Connected through hyperedge HE-2 (induced by source paper "a survey on biomedical image captioning"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "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"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"
    - "image captioning with semantic attention"

Connected through hyperedge HE-3 (induced by source paper "automated enriched medical concept generation for chest x ray images"):
  with members:
    - "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"
    - "on the automatic generation of medical imaging reports"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "deep residual learning for image recognition"
    - "interleaved text image deep mining on a large scale radiology database for automated image interpretation"

Connected through hyperedge HE-4 (induced by source paper "medical multimodal classifiers under scarce data condition"):
  with members:
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "bimodal network architectures for automatic generation of image annotation from text"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "deep residual learning for image recognition"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "towards adversarial retinal image synthesis"
    - "learning compositional visual concepts with mutual consistency"
    - "gans for biological image synthesis"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

Connected through hyperedge HE-6 (induced by source paper "attention gated networks learning to leverage salient regions in medical images"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "learn to pay attention"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "attention is all you need"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "3d fcn feature driven regression forest based pancreas localization and segmentation"

Connected through hyperedge HE-7 (induced by source paper "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"):
  with members:
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "detecting visual relationships with deep relational networks"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "areas of attention for image captioning"
    - "dual attention networks for multimodal reasoning and matching"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "semantic compositional networks for visual captioning"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"

Connected through hyperedge HE-8 (induced by source paper "attention gated networks for improving ultrasound scan plane detection"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "learn to pay attention"
    - "non local neural networks"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"
    - "residual attention network for image classification"
    - "disan directional self attention network for rnn cnn free language understanding"

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)