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: "image to markup generation with coarse to fine attention"
  Abstract: We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism. Our method is evaluated in the context of image-to-LaTeX generation, and we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup. We show that unlike neural OCR techniques using CTC-based models, attention-based approaches can tackle this non-standard OCR task. Our approach outperforms classical mathematical OCR systems by a large margin on in-domain rendered data, and, with pretraining, also performs well on out-of-domain handwritten data. To reduce the inference complexity associated with the attention-based approaches, we introduce a new coarse-to-fine attention layer that selects a support region before applying attention.

Connected through hyperedge HE-1 (induced by source paper "scene text recognition via transformer"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "icdar2019 robust reading challenge on arbitrary shaped text rrc art"
    - "2d attentional irregular scene text recognizer"
    - "a simple and robust convolutional attention network for irregular text recognition"
    - "scene text recognition from two dimensional perspective"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"

Connected through hyperedge HE-2 (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"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach"):
  with members:
    - "on nmt search errors and model errors cat got your tongue"
    - "end to end dense video captioning with masked transformer"
    - "a call for clarity in reporting bleu scores"
    - "tvqa localized compositional video question answering"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-4 (induced by source paper "sequence modeling with unconstrained generation order"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "non monotonic sequential text generation"
    - "universal transformers"
    - "middle out decoding"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "topiceq a joint topic and mathematical equation model for scientific texts"):
  with members:
    - "topic compositional neural language model"
    - "a neural knowledge language model"
    - "topically driven neural language model"
    - "discovering discrete latent topics with neural variational inference"
    - "equation parsing mapping sentences to grounded equations"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "exploring the limits of language modeling"
    - "neural variational inference for text processing"

Connected through hyperedge HE-6 (induced by source paper "image based table recognition data model and evaluation"):
  with members:
    - "tablenet deep learning model for end to end table detection and tabular data extraction from scanned document images"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "publaynet largest dataset ever for document layout analysis"
    - "rethinking table recognition using graph neural networks"
    - "extracting scientific figures with distantly supervised neural networks"
    - "corpus conversion service a machine learning platform to ingest documents at scale"
    - "mask r cnn"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "automatic generation of headlines for online math questions"):
  with members:
    - "pattern generation strategies for improving recognition of handwritten mathematical expressions"
    - "self attentive model for headline generation"
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "attention is all you need"
    - "preliminary exploration of formula embedding for mathematical information retrieval can mathematical formulae be embedded like a natural language"
    - "get to the point summarization with pointer generator networks"
    - "equation parsing mapping sentences to grounded equations"

Connected through hyperedge HE-8 (induced by source paper "pattern generation strategies for improving recognition of handwritten mathematical expressions"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"

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)