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: "publaynet largest dataset ever for document layout analysis"
  Abstract: Recognizing the layout of unstructured digital documents is an important step when parsing the documents into structured machine-readable format for downstream applications. Deep neural networks that are developed for computer vision have been proven to be an effective method to analyze layout of document images. However, document layout datasets that are currently publicly available are several magnitudes smaller than established computing vision datasets. Models have to be trained by transfer learning from a base model that is pre-trained on a traditional computer vision dataset. In this paper, we develop the PubLayNet dataset for document layout analysis by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central. The size of the dataset is comparable to established computer vision datasets, containing over 360 thousand document images, where typical document layout elements are annotated. The experiments demonstrate that deep neural networks trained on PubLayNet accurately recognize the layout of scientific articles. The pre-trained models are also a more effective base mode for transfer learning on a different document domain. We release the dataset (this https URL) to support development and evaluation of more advanced models for document layout analysis.

Connected through hyperedge HE-1 (induced by source paper "gfte graph based financial table extraction"):
  with members:
    - "complicated table structure recognition"
    - "tablebank table benchmark for image based table detection and recognition"
    - "rethinking table recognition using graph neural networks"
    - "table understanding in structured documents"

Connected through hyperedge HE-2 (induced by source paper "layoutlm pre training of text and layout for document image understanding"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "modular multimodal architecture for document classification"
    - "roberta a robustly optimized bert pretraining approach"
    - "funsd a dataset for form understanding in noisy scanned documents"
    - "chargrid towards understanding 2d documents"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (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"
    - "rethinking table recognition using graph neural networks"
    - "extracting scientific figures with distantly supervised 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.CL(Computation and Language)