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 "characterizing the impact of using features extracted from pre trained models on the quality of video captioning sequence to sequence models"):
  Abstract: The task of video captioning, that is, the automatic generation of sentences describing a sequence of actions in a video, has attracted an increasing attention recently. The complex and high-dimensional representation of video data makes it difficult for a typical encoder-decoder architectures to recognize relevant features and encode them in a proper format. Video data contains different modalities that can be recognized using a mix image, scene, action and audio features. In this paper, we characterize the different features affecting video descriptions and explore the interactions among these features and how they affect the final quality of a video representation. Building on existing encoder-decoder models that utilize limited range of video information, our comparisons show how the inclusion of multi-modal video features can make a significant effect on improving the quality of generated statements. The work is of special interest to scientists and practitioners who are using sequence-to-sequence models to generate video captions.

Includes members:
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "learning social image embedding with deep multimodal attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "webly supervised joint embedding for cross modal image text retrieval")
  Member "video captioning with transferred semantic attributes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal dense video captioning")
  Member "video captioning with multi faceted attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "video caption dataset for describing human actions in japanese")
  Member "frame and segment level features and candidate pool evaluation for video caption generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning")
  Member "soundnet learning sound representations from unlabeled video":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "hierarchical recurrent neural encoder for video representation with application to captioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "sequence to sequence video to text":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi modal dense video captioning")

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)