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: "generating lyrics with variational autoencoder and multi modal artist embeddings"
  Abstract: We present a system for generating song lyrics lines conditioned on the style of a specified artist. The system uses a variational autoencoder with artist embeddings. We propose the pre-training of artist embeddings with the representations learned by a CNN classifier, which is trained to predict artists based on MEL spectrograms of their song clips. This work is the first step towards combining audio and text modalities of songs for generating lyrics conditioned on the artist's style. Our preliminary results suggest that there is a benefit in initializing artists' embeddings with the representations learned by a spectrogram classifier.

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"
    - "deep speare a joint neural model of poetic language meter and rhyme"
    - "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 "style transfer for texts retrain report errors compare with rewrites"):
  with members:
    - "unsupervised paraphrasing without translation"
    - "style transfer as unsupervised machine translation"
    - "evaluating prose style transfer with the bible"
    - "multiple attribute text style transfer"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "guess who multilingual approach for the automated generation of author stylized poetry"

Connected through hyperedge HE-3 (induced by source paper "decomposing textual information for style transfer"):
  with members:
    - "style transfer for texts to err is human but error margins matter"
    - "style transfer as unsupervised machine translation"
    - "evaluating prose style transfer with the bible"
    - "multiple attribute text style transfer"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "guess who multilingual approach for the automated generation of author stylized poetry"

Connected through hyperedge HE-4 (induced by source paper "stylized text generation using wasserstein autoencoders with a mixture of gaussian prior"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "topic guided variational autoencoders for text generation"
    - "structuring latent spaces for stylized response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "disentangled representation learning for non parallel text style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

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)