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: "fast multi language lstm based online handwriting recognition"
  Abstract: We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous Segment-and-Decode-based system and reduced the error rate by 20%-40% relative for most languages. Further, we report new state-of-the-art results on IAM-OnDB for both the open and closed dataset setting. The system combines methods from sequence recognition with a new input encoding using Bezier curves. This leads to up to 10x faster recognition times compared to our previous system. Through a series of experiments we determine the optimal configuration of our models and report the results of our setup on a number of additional public datasets.

Connected through hyperedge HE-1 (induced by source paper "the didi dataset digital ink diagram data"):
  with members:
    - "deepwriting making digital ink editable via deep generative modeling"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-2 (induced by source paper "indylstms independently recurrent lstms"):
  with members:
    - "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"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "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 "neural architecture based on fuzzy perceptual representation for online multilingual handwriting recognition"):
  with members:
    - "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"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "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-4 (induced by source paper "a scalable handwritten text recognition system"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "going deeper with convolutions"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "generating sequences with recurrent neural networks"

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.CL(Computation and Language)