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: "evaluation of deep convolutional nets for document image classification and retrieval"
  Abstract: This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis.

Connected through hyperedge HE-1 (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"
    - "chargrid towards understanding 2d documents"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
    - "enriching word vectors with subword information"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-3 (induced by source paper "cascaded detail preserving networks for super resolution of document images"):
  with members:
    - "deep learning for image super resolution a survey"
    - "the contextual loss for image transformation with non aligned data"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "holistically nested edge detection"
    - "accurate image super resolution using very deep convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (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"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"

Connected through hyperedge HE-5 (induced by source paper "funsd a dataset for form understanding in noisy scanned documents"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a novel integrated framework for learning both text detection and recognition"
    - "mask r cnn with pyramid attention network for scene text detection"
    - "rosetta large scale system for text detection and recognition in images"
    - "east an efficient and accurate scene text detector"
    - "a survey on optical character recognition system"
    - "detecting text in natural image with connectionist text proposal network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "ddi 100 dataset for text detection and recognition"):
  with members:
    - "funsd a dataset for form understanding in noisy scanned documents"
    - "east an efficient and accurate scene text detector"
    - "synthetic data for text localisation in natural images"
    - "detecting text in natural image with connectionist text proposal network"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "microsoft coco captions data collection and evaluation server"

Connected through hyperedge HE-7 (induced by source paper "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"):
  with members:
    - "coco text dataset and benchmark for text detection and recognition in natural images"

Connected through hyperedge HE-8 (induced by source paper "hwnet v2 an efficient word image representation for handwritten documents"):
  with members:
    - "neural word search in historical manuscript collections"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "matching handwritten document images"
    - "understanding neural networks through deep visualization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)