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: "producing radiologist quality reports for interpretable artificial intelligence"
  Abstract: Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to "open the black box" of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. #R##N#We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. #R##N#The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by doctors compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.

Connected through hyperedge HE-1 (induced by source paper "clara clinical report auto completion"):
  with members:
    - "clinically accurate chest x ray report generation"
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "neural baby talk"
    - "a neural compositional paradigm for image captioning"
    - "personalized language model for query auto completion"
    - "on the automatic generation of medical imaging reports"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "learning to summarize radiology findings"

Connected through hyperedge HE-2 (induced by source paper "adversarial tcav robust and effective interpretation of intermediate layers in neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "on concept based explanations in deep neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-3 (induced by source paper "clinically accurate chest x ray report generation"):
  with members:
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "on the automatic generation of medical imaging reports"
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "learning to summarize radiology findings"
    - "unsupervised multimodal representation learning across medical images and reports"
    - "bimodal network architectures for automatic generation of image annotation from text"

Connected through hyperedge HE-4 (induced by source paper "a survey on biomedical image captioning"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "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-5 (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"
    - "icadx interpretable computer aided diagnosis of breast masses"
    - "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-6 (induced by source paper "unsupervised multimodal representation learning across medical images and reports"):
  with members:
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "learning to write notes in electronic health records"
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "universal sentence encoder"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a universal music translation network"
    - "self normalizing neural networks"

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.AI(Artificial Intelligence)