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 skimming taxonomy and comprehensive survey"):
  Abstract: Video skimming, also known as dynamic video summarization, generates a temporally abridged version of a given video. Skimming can be achieved by identifying significant components either in uni-modal or multi-modal features extracted from the video. Being dynamic in nature, video skimming, through temporal connectivity, allows better understanding of the video from its summary. Having this obvious advantage, recently, video skimming has drawn the focus of many researchers benefiting from the easy availability of the required computing resources. In this article, we provide a comprehensive survey on video skimming focusing on the substantial amount of literature from the past decade. We present a taxonomy of video skimming approaches and discuss their evolution highlighting key advances. We also provide a study on the components required for the evaluation of a video skimming performance.

Includes members:
  Member "hierarchical recurrent neural network for video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning for task driven video summarization")
  Member "a general framework for edited video and raw video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning for task driven video summarization")
  Member "phd gifs personalized highlight detection for automatic gif creation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated learning with personalization layers")
  Member "discovery of shared semantic spaces for multiscene video query and summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "i vise interactive video surveillance as an edge service using unsupervised feature queries")
  Member "summarizing first person videos from third persons points of views":
    also connected through other hyperedges:
      (none)
  Member "video summarization with attention based encoder decoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning for task driven video summarization")
  Member "query focused video summarization dataset evaluation and a memory network based approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional hierarchical attention network for query focused video summarization")
  Member "collaborative summarization of topic related videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning for task driven video summarization")

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)