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 "video storytelling textual summaries for events"):
  Abstract: Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this paper, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a residual bidirectional recurrent neural network to leverage contextual information from past and future. The multimodal embedding is then used to retrieve sentences for video clips. Second, we propose a Narrator model to select clips that are representative of the underlying storyline. The Narrator is formulated as a reinforcement learning agent, which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the video story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines and show that our method achieves better performance, in terms of quantitative measures and user study.

Includes members:
  Member "ffnet video fast forwarding via reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing")
  Member "predicting visual features from text for image and video caption retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning")
  Member "unsupervised learning of view invariant action representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "query adaptive video summarization via quality aware relevance estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional hierarchical attention network for query focused video summarization")
  Member "improved image captioning via policy gradient optimization of spider":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "dual glance model for deciphering social relationships":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised multi person action recognition in 360 circ videos")
  Member "summarization of user generated sports video by using deep action recognition features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dmc net generating discriminative motion cues for fast compressed video action recognition")
  Member "attention transfer from web images for video recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep image to video adaptation and fusion networks for action recognition")

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.MM(Multimedia)