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 a working memory model"
  Abstract: As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several neural models have been designed for this task. However, among lines of a whole poem, the coherence in meaning and topics still remains a big challenge. In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation. Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory. During the generation process, our model reads the most relevant parts from memory slots to generate the current line. After each line is generated, it writes the most salient parts of the previous line into memory slots. By dynamic manipulation of the memory, our model keeps a coherent information flow and learns to express each topic flexibly and naturally. We experiment on three different genres of Chinese poetry: quatrain, iambic and chinoiserie lyric. Both automatic and human evaluation results show that our model outperforms current state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "mixpoet diverse poetry generation via learning controllable mixed latent space"):
  with members:
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "toward controlled generation of text"
    - "flexible and creative chinese poetry generation using neural memory"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "adversarially regularized autoencoders"
    - "generating thematic chinese poetry using conditional variational autoencoders with hybrid decoders"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "pre train and plug in flexible conditional text generation with variational auto encoders"):
  with members:
    - "isolating sources of disentanglement in variational autoencoders"
    - "polite dialogue generation without parallel data"
    - "large scale gan training for high fidelity natural image synthesis"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "attention is all you need"

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"
    - "attention is all you need"
    - "towards speech to text translation without speech recognition"
    - "a deep reinforced model for abstractive summarization"
    - "coverage embedding models for neural machine translation"

Connected through hyperedge HE-4 (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"
    - "flexible and creative chinese poetry generation using neural memory"
    - "can machine generate traditional chinese poetry a feigenbaum test"
    - "chinese poetry generation with planning based neural network"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-5 (induced by source paper "prnn recurrent neural network with persistent memory"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "attention is all you need"
    - "end to end prediction of buffer overruns from raw source code via neural memory networks"
    - "deepstory video story qa by deep embedded memory networks"
    - "recurrent highway networks"
    - "pointer sentinel mixture models"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-6 (induced by source paper "shall i compare thee to a machine written sonnet an approach to algorithmic sonnet generation"):
  with members:
    - "deep speare a joint neural model of poetic language meter and rhyme"
    - "image inspired poetry generation in xiaoice"
    - "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.AI(Artificial Intelligence)