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 "keep it consistent topic aware storytelling from an image stream via iterative multi agent communication"):
  Abstract: Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we proposed a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST, where quantitative results, ablations, and human evaluation demonstrate our method's good ability in generating stories with higher quality compared to state-of-the-art methods.

Includes members:
  Member "topic aware neural keyphrase generation for social media language":
    also connected through other hyperedges:
      (none)
  Member "hierarchical photo scene encoder for album storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling")
  Member "hierarchically structured reinforcement learning for topically coherent visual story generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling")
  Member "on the automatic generation of medical imaging reports":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "a study of reinforcement learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "no metrics are perfect adversarial reward learning for visual storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "paraphrase generation with latent bag of words")
  Member "deep reinforcement learning based image captioning with embedding reward":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "hierarchically attentive rnn for album summarization and storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.CL(Computation and Language)