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 song iambics generation with neural attention based model"
  Abstract: Learning and generating Chinese poems is a charming yet challenging task. Traditional approaches involve various language modeling and machine translation techniques, however, they perform not as well when generating poems with complex pattern constraints, for example Song iambics, a famous type of poems that involve variable-length sentences and strict rhythmic patterns. This paper applies the attention-based sequence-to-sequence model to generate Chinese Song iambics. Specifically, we encode the cue sentences by a bi-directional Long-Short Term Memory (LSTM) model and then predict the entire iambic with the information provided by the encoder, in the form of an attention-based LSTM that can regularize the generation process by the fine structure of the input cues. Several techniques are investigated to improve the model, including global context integration, hybrid style training, character vector initialization and adaptation. Both the automatic and subjective evaluation results show that our model indeed can learn the complex structural and rhythmic patterns of Song iambics, and the generation is rather successful.

Connected through hyperedge HE-1 (induced by source paper "creative gans for generating poems lyrics and metaphors"):
  with members:
    - "the curious case of neural text degeneration"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuned language models for text classification"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "a deep reinforced model for abstractive summarization"
    - "word ordering without syntax"

Connected through hyperedge HE-2 (induced by source paper "neural poetry learning to generate poems using syllables"):
  with members:
    - "deep speare a joint neural model of poetic language meter and rhyme"
    - "an unsupervised character aware neural approach to word and context representation learning"
    - "adversarial generation of natural language"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "gated word character recurrent language model"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "generating chinese classical poems with rnn encoder decoder"
    - "neural paraphrase generation with stacked residual lstm networks"

Connected through hyperedge HE-3 (induced by source paper "a syllable structured contextually based conditionally generation of chinese lyrics"):
  with members:
    - "generating chinese classical poems with rnn encoder decoder"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep poetry a chinese classical poetry generation system"):
  with members:
    - "a multi modal chinese poetry generation model"
    - "generating chinese classical poems with rnn encoder decoder"

Connected through hyperedge HE-5 (induced by source paper "a multi modal chinese poetry generation model"):
  with members:
    - "flexible and creative chinese poetry generation using neural memory"
    - "can machine generate traditional chinese poetry a feigenbaum test"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "hierarchical question image co attention for visual question answering"
    - "generating chinese classical poems with rnn encoder decoder"
    - "chinese poetry generation with planning based neural network"
    - "a neural attention model for abstractive sentence summarization"
    - "backward and forward language modeling for constrained sentence generation"

Connected through hyperedge HE-6 (induced by source paper "deep speare a joint neural model of poetic language meter and rhyme"):
  with members:
    - "selective encoding for abstractive sentence summarization"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "text based lstm networks for automatic music composition"
    - "using the output embedding to improve language models"
    - "music transcription modelling and composition using deep learning"
    - "generative choreography using deep learning"

Connected through hyperedge HE-7 (induced by source paper "image inspired poetry generation in xiaoice"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "generating chinese classical poems with rnn encoder decoder"
    - "chinese poetry generation with planning based neural network"
    - "language models for image captioning the quirks and what works"
    - "microsoft coco captions data collection and evaluation server"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "how images inspire poems generating classical chinese poetry from images with memory networks"):
  with members:
    - "flexible and creative chinese poetry generation using neural memory"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "generating chinese classical poems with rnn encoder decoder"
    - "chinese poetry generation with planning based neural network"
    - "very deep convolutional networks for large scale image recognition"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "adadelta an adaptive learning rate method"

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)