human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "how images inspire poems generating classical chinese poetry from images with memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "curiosity driven reinforcement learning for diverse visual paragraph generation")
  Member "show adapt and tell adversarial training of cross domain image captioner":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "better captioning with sequence level exploration")
  Member "seqgan sequence generative adversarial nets with policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image captioning with semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "boosting image captioning with attributes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "a hierarchical approach for generating descriptive image paragraphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "generating chinese classical poems with rnn encoder decoder":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "creative gans for generating poems lyrics and metaphors")
  Member "self critical sequence training for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")

Question: Please tell me which class the source paper 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)