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: "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
  Abstract: We present and evaluate a new deep neural network architecture for automatic thoracic disease detection on chest X-rays. Deep neural networks have shown great success in a plethora of visual recognition tasks such as image classification and object detection by stacking multiple layers of convolutional neural networks (CNN) in a feed-forward manner. However, the performance gain by going deeper has reached bottlenecks as a result of the trade-off between model complexity and discrimination power. We address this problem by utilizing the recently developed routing-by agreement mechanism in our architecture. A novel characteristic of our network structure is that it extends routing to two types of layer connections (1) connection between feature maps in dense layers, (2) connection between primary capsules and prediction capsules in final classification layer. We show that our networks achieve comparable results with much fewer layers in the measurement of AUC score. We further show the combined benefits of model interpretability by generating Gradient-weighted Class Activation Mapping (Grad-CAM) for localization. We demonstrate our results on the NIH chestX-ray14 dataset that consists of 112,120 images on 30,805 unique patients including 14 kinds of lung diseases.

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"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "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 "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"
    - "combinatorial optimization with graph convolutional networks and guided tree search"

Connected through hyperedge HE-3 (induced by source paper "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "label propagation for deep semi supervised learning"
    - "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"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "deep residual learning for image recognition"

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)