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: "extracting scientific figures with distantly supervised neural networks"
  Abstract: Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction. In this paper, we induce high-quality training labels for the task of figure extraction in a large number of scientific documents, with no human intervention. To accomplish this we leverage the auxiliary data provided in two large web collections of scientific documents (arXiv and PubMed) to locate figures and their associated captions in the rasterized PDF. We share the resulting dataset of over 5.5 million induced labels---4,000 times larger than the previous largest figure extraction dataset---with an average precision of 96.8%, to enable the development of modern data-driven methods for this task. We use this dataset to train a deep neural network for end-to-end figure detection, yielding a model that can be more easily extended to new domains compared to previous work. The model was successfully deployed in Semantic Scholar,\footnote\urlhttps://www.semanticscholar.org/ a large-scale academic search engine, and used to extract figures in 13 million scientific documents.\footnoteA demo of our system is available at \urlhttp://labs.semanticscholar.org/deepfigures/,and our dataset of induced labels can be downloaded at \urlhttps://s3-us-west-2.amazonaws.com/ai2-s2-research-public/deepfigures/jcdl-deepfigures-labels.tar.gz. Code to run our system locally can be found at \urlhttps://github.com/allenai/deepfigures-open.

Connected through hyperedge HE-1 (induced by source paper "document structure extraction for forms using very high resolution semantic segmentation"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "multimodal deep networks for text and image based document classification"
    - "rethinking table parsing using graph neural networks"
    - "chargrid towards understanding 2d documents"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "video scene parsing with predictive feature learning"

Connected through hyperedge HE-2 (induced by source paper "image based table recognition data model and evaluation"):
  with members:
    - "tablenet deep learning model for end to end table detection and tabular data extraction from scanned document images"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "publaynet largest dataset ever for document layout analysis"
    - "rethinking table recognition using graph neural networks"
    - "corpus conversion service a machine learning platform to ingest documents at scale"
    - "mask r cnn"
    - "image to markup generation with coarse to fine attention"
    - "you only look once unified real time object detection"

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.DL(Digital Libraries)