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: "chinese poetry generation with planning based neural network"
  Abstract: Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user's writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning-based method can ensure that the generated poem is coherent and semantically consistent with the user's intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets.

Connected through hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long and diverse text generation with planning based hierarchical variational model"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "do massively pretrained language models make better storytellers"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "neural generation for czech data and baselines"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "end to end content and plan selection for data to text generation"
    - "rdf2pt generating brazilian portuguese texts from rdf data"
    - "are all languages equally hard to language model"
    - "predicting target language ccg supertags improves neural machine translation"
    - "modeling target side inflection in neural machine translation"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "generating classical chinese poems from vernacular chinese"):
  with members:
    - "unsupervised clinical language translation"
    - "style transfer as unsupervised machine translation"
    - "unsupervised image captioning"
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "chinese poetry generation with a working memory model"
    - "attention is all you need"
    - "towards speech to text translation without speech recognition"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical attention based seq2seq model for chinese lyrics generation"):
  with members:
    - "a survey on dialogue systems recent advances and new frontiers"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "context aware natural language generation with recurrent neural networks"
    - "tf learn tensorflow s high level module for distributed machine learning"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-5 (induced by source paper "automatic acrostic couplet generation with three stage neural network pipelines"):
  with members:
    - "weighted transformer network for machine translation"
    - "flexible and creative chinese poetry generation using neural memory"
    - "neural machine translation"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "theme aware generation model for chinese lyrics"):
  with members:
    - "attention is all you need"
    - "topic aware neural response generation"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "neural responding machine for short text conversation"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "an iterative polishing framework based on quality aware masked language model for chinese poetry generation"):
  with members:
    - "pretraining based natural language generation for text summarization"
    - "chinese poetry generation with a salient clue mechanism"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "chinese poetry generation with a working memory model"
    - "flexible and creative chinese poetry generation using neural memory"
    - "can machine generate traditional chinese poetry a feigenbaum test"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-8 (induced by source paper "gpt based generation for classical chinese poetry"):
  with members:
    - "the curious case of neural text degeneration"
    - "chinese poetry generation with a salient clue mechanism"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

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)