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: "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
  Abstract: In this study, we present a novel end-to-end approach based on the encoder-decoder framework with the attention mechanism for online handwritten mathematical expression recognition (OHMER). First, the input two-dimensional ink trajectory information of handwritten expression is encoded via the gated recurrent unit based recurrent neural network (GRU-RNN). Then the decoder is also implemented by the GRU-RNN with a coverage-based attention model. The proposed approach can simultaneously accomplish the symbol recognition and structural analysis to output a character sequence in LaTeX format. Validated on the CROHME 2014 competition task, our approach significantly outperforms the state-of-the-art with an expression recognition accuracy of 52.43% by only using the official training dataset. Furthermore, the alignments between the input trajectories of handwritten expressions and the output LaTeX sequences are visualized by the attention mechanism to show the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "stroke constrained attention network for online handwritten mathematical expression recognition"):
  with members:
    - "cross modal self attention network for referring image segmentation"
    - "compact trilinear interaction for visual question answering"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "attention aware compositional network for person re identification"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-2 (induced by source paper "translating mathematical formula images to latex sequences using deep neural networks with sequence level training"):
  with members:
    - "a study of reinforcement learning for neural machine translation"
    - "attention is all you need"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "what you get is what you see a visual markup decompiler"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "effective approaches to attention based neural machine translation"
    - "minimum risk training for neural machine translation"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "fast multi language lstm based online handwriting recognition"):
  with members:
    - "sequence to label script identification for multilingual ocr"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "neural architecture based on fuzzy perceptual representation for online multilingual handwriting recognition"):
  with members:
    - "fast multi language lstm based online handwriting recognition"
    - "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "a hybrid nn hmm modeling technique for online arabic handwriting recognition"

Connected through hyperedge HE-5 (induced by source paper "sliding line point regression for shape robust scene text detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "fused text segmentation networks for multi oriented scene text detection"

Connected through hyperedge HE-6 (induced by source paper "multi scale attention with dense encoder for handwritten mathematical expression recognition"):
  with members:
    - "trajectory based radical analysis network for online handwritten chinese character recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "densely connected convolutional networks"
    - "modeling coverage for neural machine translation"
    - "what you get is what you see a visual markup decompiler"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "trajectory based radical analysis network for online handwritten chinese character recognition"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "modeling coverage for neural machine translation"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character 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)