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: "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
  Abstract: We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half. Existing approaches, such as the DeepDoc-Classifier, apply standard Convolutional Network architectures with transfer learning from the object recognition domain. The contribution of the paper is threefold: First, it investigates recently introduced very deep neural network architectures (GoogLeNet, VGG, ResNet) using transfer learning (from real images). Second, it proposes transfer learning from a huge set of document images, i.e. 400; 000 documents. Third, it analyzes the impact of the amount of training data (document images) and other parameters to the classification abilities. We use two datasets, the Tobacco-3482 and the large-scale RVL-CDIP dataset. We achieve an accuracy of 91:13% for the Tobacco-3482 dataset while earlier approaches reach only 77:6%. Thus, a relative error reduction of more than 60% is achieved. For the large dataset RVL-CDIP, an accuracy of 90:97% is achieved, corresponding to a relative error reduction of 11:5%.

Connected through hyperedge HE-1 (induced by source paper "multimodal deep networks for text and image based document classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "magnitude a fast efficient universal vector embedding utility package"
    - "deep contextualized word representations"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "mimicking word embeddings using subword rnns"
    - "enriching word vectors with subword information"
    - "xception deep learning with depthwise separable convolutions"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "layoutlm pre training of text and layout for document image understanding"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "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"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "recognizing challenging handwritten annotations with fully convolutional networks"):
  with members:
    - "real time document image classification using deep cnn and extreme learning machines"
    - "document image binarization with fully convolutional neural networks"
    - "sequence to label script identification for multilingual ocr"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "document image classification with intra domain transfer learning and stacked generalization of deep convolutional neural networks"):
  with members:
    - "analysis of convolutional neural networks for document image classification"
    - "what is the right way to represent document images"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "multilevel context representation for improving object recognition"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "identity mappings in deep residual networks"
    - "generative adversarial text to image synthesis"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "stackgan text to photo realistic image synthesis with stacked generative adversarial networks"
    - "recent advances in convolutional neural networks"
    - "deep residual learning for image recognition"

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)