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Center node: "fully convolutional neural networks for page segmentation of historical document images"
  Abstract: We propose a high-performance fully convolutional neural network (FCN) for historical document segmentation that is designed to process a single page in one step. The advantage of this model beside its speed is its ability to directly learn from raw pixels instead of using preprocessing steps e. g. feature computation or superpixel generation. We show that this network yields better results than existing methods on different public data sets. For evaluation of this model we introduce a novel metric that is independent of ambiguous ground truth called Foreground Pixel Accuracy (FgPA). This pixel based measure only counts foreground pixels in the binarized page, any background pixel is omitted. The major advantage of this metric is, that it enables researchers to compare different segmentation methods on their ability to successfully segment text or pictures and not on their ability to learn and possibly overfit the peculiarities of an ambiguous hand-made ground truth segmentation.

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"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mask r cnn"

Connected through hyperedge HE-2 (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"
    - "chargrid towards understanding 2d documents"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "video scene parsing with predictive feature learning"

Connected through hyperedge HE-3 (induced by source paper "ocr4all an open source tool providing a semi automatic ocr workflow for historical printings"):
  with members:
    - "a two stage method for text line detection in historical documents"
    - "evaluating sequence to sequence models for handwritten text recognition"
    - "calamari a high performance tensorflow based deep learning package for optical character recognition"
    - "improving ocr accuracy on early printed books by utilizing cross fold training and voting"
    - "state of the art optical character recognition of 19th century fraktur scripts using open source engines"
    - "ground truth for training ocr engines on historical documents in german fraktur and early modern latin"
    - "larex a semi automatic open source tool for layout analysis and region extraction on early printed books"
    - "profiling of ocr ed historical texts revisited"

Connected through hyperedge HE-4 (induced by source paper "indiscapes instance segmentation networks for layout parsing of historical indic manuscripts"):
  with members:
    - "focal loss for dense object detection"
    - "case study of a highly automated layout analysis and ocr of an incunabulum der heiligen leben 1488"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "feature pyramid networks for object detection"
    - "ocr of historical printings with an application to building diachronic corpora a case study using the ridges herbal corpus"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (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"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "cloudscan a configuration free invoice analysis system using recurrent neural networks"
    - "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"

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)