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: "joint line segmentation and transcription for end to end handwritten paragraph recognition"
  Abstract: Offline handwriting recognition systems require cropped text line images for both training and recognition. On the one hand, the annotation of position and transcript at line level is costly to obtain. On the other hand, automatic line segmentation algorithms are prone to errors, compromising the subsequent recognition. In this paper, we propose a modification of the popular and efficient multi-dimensional long short-term memory recurrent neural networks (MDLSTM-RNNs) to enable end-to-end processing of handwritten paragraphs. More particularly, we replace the collapse layer transforming the two-dimensional representation into a sequence of predictions by a recurrent version which can recognize one line at a time. In the proposed model, a neural network performs a kind of implicit line segmentation by computing attention weights on the image representation. The experiments on paragraphs of Rimes and IAM database yield results that are competitive with those of networks trained at line level, and constitute a significant step towards end-to-end transcription of full documents.

Connected through hyperedge HE-1 (induced by source paper "decoupled attention network for text recognition"):
  with members:
    - "aggregation cross entropy for sequence recognition"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "scene text detection and recognition the deep learning era"
    - "attention is all you need"
    - "aon towards arbitrarily oriented text recognition"

Connected through hyperedge HE-2 (induced by source paper "aggregation cross entropy for sequence recognition"):
  with members:
    - "edit probability for scene text recognition"
    - "attention is all you need"
    - "scene text recognition with sliding convolutional character models"
    - "focusing attention towards accurate text recognition in natural images"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "evaluating sequence to sequence models for handwritten text recognition"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "attention is all you need"
    - "label organized memory augmented neural network"
    - "convolutional sequence to sequence learning"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "a two stage method for text line detection in historical documents"):
  with members:
    - "baseline detection in historical documents using convolutional u nets"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "multi task handwritten document layout analysis"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "full page text recognition learning where to start and when to stop"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "regular expressions for decoding of neural network outputs"

Connected through hyperedge HE-5 (induced by source paper "a computationally efficient pipeline approach to full page offline handwritten text recognition"):
  with members:
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "attention is all you need"
    - "neural networks for text correction and completion in keyboard decoding"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "ssd single shot multibox detector"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "deeperase weakly supervised ink artifact removal in document text images"):
  with members:
    - "document image binarization with fully convolutional neural networks"
    - "character based handwritten text transcription with attention networks"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "detecting text in natural image with connectionist text proposal network"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "lifelong learning for text retrieval and recognition in historical handwritten document collections"):
  with members:
    - "a large scale field test on word image classification in large historical document collections using a traditional and two deep learning methods"
    - "open set chinese character recognition using multi typed attributes"
    - "learning deep representations for word spotting under weak supervision"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "textnet irregular text reading from images with an end to end trainable network"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "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"

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)