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: "deep speare a joint neural model of poetic language meter and rhyme"
  Abstract: In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgements. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems. Expert evaluation, however, reveals that a vanilla language model captures meter implicitly, and that machine-generated poems still underperform in terms of readability and emotion. Our research shows the importance expert evaluation for poetry generation, and that future research should look beyond rhyme/meter and focus on poetic language.

Connected through hyperedge HE-1 (induced by source paper "introducing aspects of creativity in automatic poetry generation"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "beyond narrative description generating poetry from images by multi adversarial training"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "deep reinforcement learning for dialogue generation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-2 (induced by source paper "neural poetry learning to generate poems using syllables"):
  with members:
    - "an unsupervised character aware neural approach to word and context representation learning"
    - "adversarial generation of natural language"
    - "chinese song iambics generation with neural attention based model"
    - "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 "learning rhyming constraints using structured adversaries"):
  with members:
    - "answer based adversarial training for generating clarification questions"
    - "discourse aware neural rewards for coherent text generation"
    - "learning to write with cooperative discriminators"
    - "neural dynamic programming for musical self similarity"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "sentence simplification with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-4 (induced by source paper "shall i compare thee to a machine written sonnet an approach to algorithmic sonnet generation"):
  with members:
    - "image inspired poetry generation in xiaoice"
    - "chinese poetry generation with a working memory model"
    - "beyond narrative description generating poetry from images by multi adversarial training"
    - "chinese poetry generation with planning based neural network"

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)