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 "rethinking the evaluation of video summaries"):
  Abstract: Video summarization is a technique to create a short skim of the original video while preserving the main stories/content. There exists a substantial interest in automatizing this process due to the rapid growth of the available material. The recent progress has been facilitated by public benchmark datasets, which enable easy and fair comparison of methods. Currently the established evaluation protocol is to compare the generated summary with respect to a set of reference summaries provided by the dataset. In this paper, we will provide in-depth assessment of this pipeline using two popular benchmark datasets. Surprisingly, we observe that randomly generated summaries achieve comparable or better performance to the state-of-the-art. In some cases, the random summaries outperform even the human generated summaries in leave-one-out experiments. Moreover, it turns out that the video segmentation, which is often considered as a fixed pre-processing method, has the most significant impact on the performance measure. Based on our observations, we propose alternative approaches for assessing the importance scores as well as an intuitive visualization of correlation between the estimated scoring and human annotations.

Includes members:
  Member "query adaptive video summarization via quality aware relevance estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video storytelling textual summaries for events")
  Member "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation")
  Member "real time video highlights for yahoo esports":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards extracting highlights from recorded live videos an implicit crowdsourcing approach")
  Member "summary transfer exemplar based subset selection for video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "video storytelling textual summaries for events")
  Member "video summarization with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "video storytelling textual summaries for events")
  Member "query focused extractive video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional hierarchical attention network for query focused video summarization")
  Member "video summarization using deep semantic features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional hierarchical attention network for query focused video summarization")
  Member "videoset video summary evaluation through text":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "video storytelling textual summaries for events")

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)