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: "convolutional neural networks for font classification"
  Abstract: Classifying pages or text lines into font categories aids transcription because single font Optical Character Recognition (OCR) is generally more accurate than omni-font OCR. We present a simple framework based on Convolutional Neural Networks (CNNs), where a CNN is trained to classify small patches of text into predefined font classes. To classify page or line images, we average the CNN predictions over densely extracted patches. We show that this method achieves state-of-the-art performance on a challenging dataset of 40 Arabic computer fonts with 98.8\% line level accuracy. This same method also achieves the highest reported accuracy of 86.6% in predicting paleographic scribal script classes at the page level on medieval Latin manuscripts. Finally, we analyze what features are learned by the CNN on Latin manuscripts and find evidence that the CNN is learning both the defining morphological differences between scribal script classes as well as overfitting to class-correlated nuisance factors. We propose a novel form of data augmentation that improves robustness to text darkness, further increasing classification performance.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis"):
  with members:
    - "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"
    - "do better imagenet models transfer better"
    - "offline signature verification by combining graph edit distance and triplet networks"
    - "deep learning for classical japanese literature"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "identifying cross depicted historical motifs"
    - "open evaluation tool for layout analysis of document images"
    - "historical document image segmentation with lda initialized deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep generalized max pooling"):
  with members:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "semi supervised feature learning for improving writer identification"
    - "on the convergence of adam and beyond"
    - "precision learning towards use of known operators in neural networks"
    - "in defense of the triplet loss for person re identification"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "encoding cnn activations for writer recognition"
    - "phocnet a deep convolutional neural network for word spotting in handwritten 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)