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: "handwriting trajectory recovery using end to end deep encoder decoder network"
  Abstract: In this paper, we introduce a novel technique to recover the pen trajectory of offline characters which is a crucial step for handwritten character recognition. Generally, online acquisition approach has more advantage than its offline counterpart as the online technique keeps track of the pen movement. Hence, pen tip trajectory retrieval from offline text can bridge the gap between online and offline methods. Our proposed framework employs sequence to sequence model which consists of an encoder-decoder LSTM module. Our encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation. The output of the encoder is fed to a decoder LSTM and we get the successive coordinate points from every time step of the decoder LSTM. Although the sequence to sequence model is a popular paradigm in various computer vision and language translation tasks, the main contribution of our work lies in designing an end-to-end network for a decade old popular problem in Document Image Analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit dataset are used for our experiments. Our proposed method has achieved superior performance compared to the other conventional approaches.

Connected through hyperedge HE-1 (induced by source paper "script identification in natural scene image and video frames using an attention based convolutional lstm network"):
  with members:
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "staff line removal using generative adversarial networks"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "image captioning with semantic attention"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fine grained approach to scene text script identification"
    - "understanding deep neural networks with rectified linear units"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"

Connected through hyperedge HE-2 (induced by source paper "modality conversion of handwritten patterns by cross variational autoencoders"):
  with members:
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "cross modal deep variational hand pose estimation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "generating sentences from a continuous space"
    - "learning deconvolution network for semantic segmentation"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-3 (induced by source paper "indic handwritten script identification using offline online multimodal deep network"):
  with members:
    - "deep learning for word level handwritten indic script identification"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "hmm based writer identification in music score documents without staff line removal"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "hmm based indic handwritten word recognition using zone segmentation"
    - "local binary pattern for word spotting in handwritten historical document"
    - "compact bilinear pooling"

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)