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: "drawing and recognizing chinese characters with recurrent neural network"
  Abstract: Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.

Connected through hyperedge HE-1 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "indylstms independently recurrent lstms"):
  with members:
    - "fast multi language lstm based online handwriting recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "quasi recurrent neural networks"
    - "neural architecture search with reinforcement learning"
    - "a recurrent neural network without chaos"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "dropout improves recurrent neural networks for handwriting recognition"

Connected through hyperedge HE-3 (induced by source paper "feature engineering and forecasting via derivative free optimization and ensemble of sequence to sequence networks with applications in renewable energy"):
  with members:
    - "multivariate multistep forecasting reconstruction and feature selection of ocean waves via recurrent and sequence to sequence networks"
    - "lstm a search space odyssey"
    - "the microsoft 2017 conversational speech recognition system"
    - "black box optimization in machine learning with trust region based derivative free algorithm"
    - "lung cancer screening using adaptive memory augmented recurrent networks"
    - "text based lstm networks for automatic music composition"
    - "learning to diagnose with lstm recurrent neural networks"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "fast multi language lstm based online handwriting recognition"):
  with members:
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "sequence to label script identification for multilingual ocr"
    - "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-5 (induced by source paper "auto rotating perceptrons"):
  with members:
    - "activation functions comparison of trends in practice and research for deep learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "revise saturated activation functions"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"
    - "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-7 (induced by source paper "knn and ann based recognition of handwritten pashto letters using zoning features"):
  with members:
    - "indic handwritten script identification using offline online multimodal deep network"
    - "exponentiated gradient exploration for active learning"

Connected through hyperedge HE-8 (induced by source paper "learning to sketch with shortcut cycle consistency"):
  with members:
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "fader networks manipulating images by sliding attributes"
    - "a neural representation of sketch drawings"
    - "dualgan unsupervised dual learning for image to image translation"
    - "instance normalization the missing ingredient for fast stylization"

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)