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: "a saliency based convolutional neural network for table and chart detection in digitized documents"
  Abstract: Deep Convolutional Neural Networks (DCNNs) have recently been applied successfully to a variety of vision and multimedia tasks, thus driving development of novel solutions in several application domains. Document analysis is a particularly promising area for DCNNs: indeed, the number of available digital documents has reached unprecedented levels, and humans are no longer able to discover and retrieve all the information contained in these documents without the help of automation. Under this scenario, DCNNs offers a viable solution to automate the information extraction process from digital documents. Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis carried out on an extended version of ICDAR 2013 (with annotated charts as well as tables) shows that our approach yields promising results, outperforming existing models.

Connected through hyperedge HE-1 (induced by source paper "tablenet deep learning model for end to end table detection and tabular data extraction from scanned document images"):
  with members:
    - "multidomain document layout understanding using few shot object detection"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "table understanding in structured documents"):
  with members:
    - "yolov3 an incremental improvement"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "graph classification with 2d convolutional neural networks"
    - "learning convolutional neural networks for graphs"
    - "detecting table region in pdf documents using distant supervision"
    - "locating tables in scanned documents for reconstructing and republishing iciafs14"

Connected through hyperedge HE-3 (induced by source paper "rethinking table recognition using graph neural networks"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "deformable convolutional networks"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "fast r cnn"

Connected through hyperedge HE-4 (induced by source paper "rethinking table parsing using graph neural networks"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "deformable convolutional networks"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "publaynet largest dataset ever for document layout analysis"):
  with members:
    - "corpus conversion service a machine learning platform to ingest documents at scale"
    - "mask r cnn"
    - "faster r cnn towards real time object detection with region proposal 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.CV(Computer Vision and Pattern Recognition)