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 "a hierarchical approach for visual storytelling using image description"):
  Abstract: One of the primary challenges of visual storytelling is developing techniques that can maintain the context of the story over long event sequences to generate human-like stories. In this paper, we propose a hierarchical deep learning architecture based on encoder-decoder networks to address this problem. To better help our network maintain this context while also generating long and diverse sentences, we incorporate natural language image descriptions along with the images themselves to generate each story sentence. We evaluate our system on the Visual Storytelling (VIST) dataset and show that our method outperforms state-of-the-art techniques on a suite of different automatic evaluation metrics. The empirical results from this evaluation demonstrate the necessities of different components of our proposed architecture and shows the effectiveness of the architecture for visual storytelling.

Includes members:
  Member "contextualize show and tell a neural visual storyteller":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visual storytelling via predicting anchor word embeddings in the stories")
  Member "a pipeline for creative visual storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "my way of telling a story persona based grounded story generation")
  Member "plan and write towards better automatic storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "no metrics are perfect adversarial reward learning for visual storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "paraphrase generation with latent bag of words")
  Member "glac net glocal attention cascading networks for multi image cued story generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "my way of telling a story persona based grounded story generation")
  Member "stories for images in sequence by using visual and narrative components":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "my way of telling a story persona based grounded story generation")
  Member "event representations for automated story generation with deep neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "hierarchically attentive rnn for album summarization and storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling")

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)