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: "mdnet a semantically and visually interpretable medical image diagnosis network"
  Abstract: The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.

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"
    - "producing radiologist quality reports for interpretable artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "towards interpretable and robust hand detection via pixel wise prediction"):
  with members:
    - "scale invariant fully convolutional network detecting hands efficiently"
    - "east an efficient and accurate scene text detector"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "simple online and realtime tracking with a deep association metric"
    - "brain tumor segmentation based on refined fully convolutional neural networks with a hierarchical dice loss"
    - "simple online and realtime tracking"
    - "ssd single shot multibox detector"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "oralcam enabling self examination and awareness of oral health using a smartphone camera"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "a survey on deep learning in medical image analysis"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "an all in one convolutional neural network for face analysis"

Connected through hyperedge HE-4 (induced by source paper "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"):
  with members:
    - "a self attention based deep learning method for lesion attribute detection from ct reports"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "imbalanced deep learning by minority class incremental rectification"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (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"
    - "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"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "a survey on deep learning in medical image analysis"
    - "image captioning with semantic attention"

Connected through hyperedge HE-7 (induced by source paper "controllable text to image generation"):
  with members:
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "semantic image synthesis via adversarial learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural photo editing with introspective adversarial networks"
    - "learning what and where to draw"
    - "plug play generative networks conditional iterative generation of images in latent space"

Connected through hyperedge HE-8 (induced by source paper "chip channel wise disentangled interpretation of deep convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "libra r cnn towards balanced learning for object detection"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "mask r cnn"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "ask your neurons a deep learning approach to visual question answering"
    - "the mythos of model interpretability"

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)