human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "document structure extraction for forms using very high resolution semantic segmentation"):
  Abstract: In this work, we look at the problem of structure extraction from document images with a specific focus on forms. Forms as a document class have not received much attention, even though they comprise a significant fraction of documents and enable several applications. Forms possess a rich, complex, hierarchical, and high-density semantic structure that poses several challenges to semantic segmentation methods. We propose a prior based deep CNN-RNN hierarchical network architecture that enables document structure extraction using very high resolution(1800 x 1000) images. We divide the document image into overlapping horizontal strips such that the network segments a strip and uses its prediction mask as prior while predicting the segmentation for the subsequent strip. We perform experiments establishing the effectiveness of our strip based network architecture through ablation methods and comparison with low-resolution variations. We introduce our new rich human-annotated forms dataset, and we show that our method significantly outperforms other segmentation baselines in extracting several hierarchical structures on this dataset. We also outperform other baselines in table detection task on the Marmot dataset. Our method is currently being used in a world-leading customer experience management software suite for automated conversion of paper and PDF forms to modern HTML based forms.

Includes members:
  Member "graph convolution for multimodal information extraction from visually rich documents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lambert layout aware language modeling using bert for information extraction")
  Member "multimodal deep networks for text and image based document classification":
    also connected through other hyperedges:
      (none)
  Member "rethinking table parsing using graph neural networks":
    also connected through other hyperedges:
      (none)
  Member "extracting scientific figures with distantly supervised neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image based table recognition data model and evaluation")
  Member "chargrid towards understanding 2d documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "fully convolutional neural networks for page segmentation of historical document images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)