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: "chargrid towards understanding 2d documents"
  Abstract: We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.

Connected through hyperedge HE-1 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers"):
  with members:
    - "using word embeddings to examine gender bias in dutch newspapers 1950 1990"
    - "the via annotation software for images audio and video"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "deep contextualized word representations"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "lambert layout aware language modeling using bert for information extraction"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "identifying table structure in documents using conditional generative adversarial networks"):
  with members:
    - "extracting tables from documents using conditional generative adversarial networks and genetic algorithms"
    - "which training methods for gans do actually converge"
    - "instance normalization the missing ingredient for fast stylization"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "kleister a novel task for information extraction involving long documents with complex layout"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "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"
    - "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-5 (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"
    - "extracting scientific figures with distantly supervised neural networks"
    - "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-6 (induced by source paper "chargrid ocr end to end trainable optical character recognition for printed documents using instance segmentation"):
  with members:
    - "convolutional character networks"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "east an efficient and accurate scene text detector"
    - "aon towards arbitrarily oriented text recognition"
    - "mask r cnn"
    - "recursive recurrent nets with attention modeling for ocr in the wild"

Connected through hyperedge HE-7 (induced by source paper "graph convolution for multimodal information extraction from visually rich documents"):
  with members:
    - "n ary relation extraction using graph state lstm"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "image generation from scene graphs"
    - "cross sentence n ary relation extraction with graph lstms"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "cloudscan a configuration free invoice analysis system using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "named entity recognition is there a glass ceiling"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "a survey on deep learning for named entity recognition"
    - "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"

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)