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: "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
  Abstract: We present an attention-based model for end-to-end handwriting recognition. Our system does not require any segmentation of the input paragraph. The model is inspired by the differentiable attention models presented recently for speech recognition, image captioning or translation. The main difference is the covert and overt attention, implemented as a multi-dimensional LSTM network. Our principal contribution towards handwriting recognition lies in the automatic transcription without a prior segmentation into lines, which was crucial in previous approaches. To the best of our knowledge this is the first successful attempt of end-to-end multi-line handwriting recognition. We carried out experiments on the well-known IAM Database. The results are encouraging and bring hope to perform full paragraph transcription in the near future.

Connected through hyperedge HE-1 (induced by source paper "towards pure end to end learning for recognizing multiple text sequences from an image"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "edit probability for scene text recognition"
    - "textboxes a single shot oriented scene text detector"
    - "an anchor free region proposal network for faster r cnn based text detection approaches"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "towards end to end text spotting with convolutional recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "neural word search in historical manuscript collections"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "information theoretical label embeddings for large scale image classification"
    - "identity mappings in deep residual networks"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "matching handwritten document images"

Connected through hyperedge HE-3 (induced by source paper "unsupervised writer adaptation for synthetic to real handwritten word recognition"):
  with members:
    - "evaluating sequence to sequence models for handwritten text recognition"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "hwnet v2 an efficient word image representation for handwritten documents"
    - "boosting handwriting text recognition in small databases with transfer learning"
    - "adversarial discriminative domain adaptation"
    - "zero shot deep domain adaptation"
    - "learning deep representations for word spotting under weak supervision"
    - "generating synthetic data for text recognition"

Connected through hyperedge HE-4 (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"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "treynet a neural model for text localization transcription and named entity recognition in full pages"):
  with members:
    - "joint recognition of handwritten text and named entities with a neural end to end model"
    - "attend copy parse end to end information extraction from documents"
    - "focal loss for dense object detection"
    - "an overview of multi task learning in deep neural networks"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "neural architectures for named entity recognition"
    - "you only look once unified real time object detection"

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"
    - "detecting text in natural image with connectionist text proposal network"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "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 "accurate data efficient unconstrained text recognition with convolutional neural networks"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "scene text recognition from two dimensional perspective"
    - "handwriting recognition of historical documents with few labeled data"
    - "autoaugment learning augmentation policies from data"
    - "attention u net learning where to look for the pancreas"
    - "how many labeled license plates are needed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

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)