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: "graph convolution for multimodal information extraction from visually rich documents"
  Abstract: Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.

Connected through hyperedge HE-1 (induced by source paper "lambert layout aware language modeling using bert for information extraction"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "m bert injecting multimodal information in the bert structure"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "chargrid towards understanding 2d documents"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "kleister a novel task for information extraction involving long documents with complex layout"):
  with members:
    - "cutie learning to understand documents with convolutional universal text information extractor"
    - "one shot information extraction from document images using neuro deductive program synthesis"
    - "roberta a robustly optimized bert pretraining approach"
    - "funsd a dataset for form understanding in noisy scanned documents"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikireading a novel large scale language understanding task over wikipedia"

Connected through hyperedge HE-3 (induced by source paper "document structure extraction for forms using very high resolution semantic segmentation"):
  with members:
    - "multimodal deep networks for text and image based document classification"
    - "rethinking table parsing using graph neural networks"
    - "extracting scientific figures with distantly supervised 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-4 (induced by source paper "named entity recognition is there a glass ceiling"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "a survey on deep learning for named entity recognition"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "a survey on open information extraction"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-5 (induced by source paper "layoutlm pre training of text and layout for document image understanding"):
  with members:
    - "modular multimodal architecture for document classification"
    - "publaynet largest dataset ever for document layout analysis"
    - "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"

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.IR(Information Retrieval)