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: "natural language generation by hierarchical decoding with linguistic patterns"
  Abstract: Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains an encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple cross entropy loss training criterion. However, the simple encoder-decoder architecture usually suffers from generating complex and long sentences, because the decoder has to learn all grammar and diction knowledge. This paper introduces a hierarchical decoding NLG model based on linguistic patterns in different levels, and shows that the proposed method outperforms the traditional one with a smaller model size. Furthermore, the design of the hierarchical decoding is flexible and easily-extensible in various NLG systems.

Connected through hyperedge HE-1 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"

Connected through hyperedge HE-2 (induced by source paper "bridging dialogue generation and facial expression synthesis"):
  with members:
    - "emotionlines an emotion corpus of multi party conversations"
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "integrating planning for task completion dialogue policy learning"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "dual supervised learning for natural language understanding and generation"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "integrating planning for task completion dialogue policy learning"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "dual supervised learning"
    - "listening while speaking speech chain by deep learning"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"

Connected through hyperedge HE-4 (induced by source paper "tree structured semantic encoder with knowledge sharing for domain adaptation in natural language generation"):
  with members:
    - "variational cross domain natural language generation for spoken dialogue systems"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "the e2e dataset new challenges for end to end generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "improved semantic representations from tree structured long short term memory networks"
    - "multi domain dialog state tracking using recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "generating long and informative reviews with aspect aware coarse to fine decoding"):
  with members:
    - "global encoding for abstractive summarization"
    - "learning neural templates for text generation"
    - "hierarchical neural story generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "neural document summarization by jointly learning to score and select sentences"
    - "long text generation via adversarial training with leaked information"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "a stable variational autoencoder for text modelling"):
  with members:
    - "learning neural templates for text generation"
    - "spherical latent spaces for stable variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "an architecture for deep hierarchical generative models"
    - "generating sentences from a continuous space"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "end to end content and plan selection for data to text generation"):
  with members:
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "unsupervised natural language generation with denoising autoencoders"
    - "findings of the e2e nlg challenge"
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "get to the point summarization with pointer generator networks"
    - "addressing the data sparsity issue in neural amr parsing"
    - "the e2e dataset new challenges for end to end generation"

Connected through hyperedge HE-8 (induced by source paper "investigating linguistic pattern ordering in hierarchical natural language generation"):
  with members:
    - "end to end task completion neural dialogue systems"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "the e2e dataset new challenges for end to end generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "learning end to end goal oriented dialog"
    - "scheduled sampling for sequence prediction 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)