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: "beyond narrative description generating poetry from images by multi adversarial training"
  Abstract: Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with multiple lines) to an image for automatic poetry creation. This task involves multiple challenges, including discovering poetic clues from the image (e.g., hope from green), and generating poems to satisfy both relevance to the image and poeticness in language level. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. To extract poetic clues from images, we propose to learn a deep coupled visual-poetic embedding, in which the poetic representation from objects, sentiments \footnoteWe consider both adjectives and verbs that can express emotions and feelings as sentiment words in this research. and scenes in an image can be jointly learned. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator. To facilitate the research, we have released two poem datasets by human annotators with two distinct properties: 1) the first human annotated image-to-poem pair dataset (with $8,292$ pairs in total), and 2) to-date the largest public English poem corpus dataset (with $92,265$ different poems in total). Extensive experiments are conducted with 8K images, among which 1.5K image are randomly picked for evaluation. Both objective and subjective evaluations show the superior performances against the state-of-the-art methods for poem generation from images. Turing test carried out with over $500$ human subjects, among which 30 evaluators are poetry experts, demonstrates the effectiveness of our approach.

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"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "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 storyboard artist visualizing stories with coherent image sequences"):
  with members:
    - "videobert a joint model for video and language representation learning"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "stacked cross attention for image text matching"
    - "to create what you tell generating videos from captions"
    - "learning word vectors for 157 languages"
    - "storygan a sequential conditional gan for story visualization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning and sub word unit approach in written art generation"):
  with members:
    - "image inspired poetry generation in xiaoice"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "guess who multilingual approach for the automated generation of author stylized poetry"
    - "universal language model fine tuning for text classification"
    - "flexible and creative chinese poetry generation using neural memory"
    - "cyclical learning rates for training neural networks"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-4 (induced by source paper "prose for a painting"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "pointer sentinel mixture models"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial training in affective computing and sentiment analysis recent advances and perspectives"):
  with members:
    - "geometry contrastive gan for facial expression transfer"
    - "generative adversarial networks an overview"
    - "towards open set identity preserving face synthesis"
    - "talking face generation by conditional recurrent adversarial network"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "interactive generative adversarial networks for facial expression generation in dyadic interactions"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "voice impersonation using generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "a wasserstein gan model with the total variational regularization"):
  with members:
    - "banach wasserstein gan"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-7 (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"
    - "chinese poetry generation with a working memory model"
    - "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.CV(Computer Vision and Pattern Recognition)