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: "cloudscan a configuration free invoice analysis system using recurrent neural networks"
  Abstract: We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts. The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts. For the harder task of unseen invoice layouts, the recurrent neural network model outperforms the baseline with 0.840 average F1 compared to 0.788.

Connected through hyperedge HE-1 (induced by source paper "graph convolution for multimodal information extraction from visually rich documents"):
  with members:
    - "n ary relation extraction using graph state lstm"
    - "chargrid towards understanding 2d documents"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "cutie learning to understand documents with convolutional universal text information extractor"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "one shot template matching for automatic document data capture"):
  with members:
    - "table understanding in structured documents"
    - "yolov3 an incremental improvement"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "attend copy parse end to end information extraction from documents"):
  with members:
    - "rosetta large scale system for text detection and recognition in images"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "get to the point summarization with pointer generator networks"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "one shot learning with memory augmented neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-5 (induced by source paper "chargrid towards understanding 2d documents"):
  with members:
    - "document image binarization with fully convolutional neural networks"
    - "focal loss for dense object detection"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "learning to extract semantic structure from documents using multimodal fully convolutional neural network"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "neural architectures for named entity recognition"
    - "multilingual language processing from bytes"
    - "a neural attention model for abstractive sentence summarization"

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)