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 "multi stream dynamic video summarization"):
  Abstract: With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content. Nonetheless, the spread of social and egocentric cameras creates an abundance of sparse scenarios captured by several devices, and ultimately required to be jointly summarized. In this paper, we discuss the problem of summarizing videos recorded simultaneously by several dynamic cameras that intermittently share the field of view. We present a robust framework that (a) identifies a diverse set of important events among moving cameras that often are not capturing the same scene, and (b) selects the most representative view(s) at each event to be included in a universal summary. Due to the lack of an applicable alternative, we collected a new multi-view egocentric dataset, Multi-Ego. Our dataset is recorded simultaneously by three cameras, covering a wide variety of real-life scenarios. The footage is annotated by multiple individuals under various summarization configurations, with a consensus analysis ensuring a reliable ground truth. We conduct extensive experiments on the compiled dataset in addition to three other standard benchmarks that show the robustness and the advantage of our approach in both supervised and unsupervised settings. Additionally, we show that our approach learns collectively from data of varied number-of-views and orthogonal to other summarization methods, deeming it scalable and generic. Our materials are made publicly available.

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 "video summarization via actionness ranking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation")
  Member "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks")
  Member "improving sequential determinantal point processes for supervised video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diversity in machine learning")
  Member "diversity aware multi video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coin a large scale dataset for comprehensive instructional video analysis")
  Member "identifying first person camera wearers in third person videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis")
  Member "video summarization with attention based encoder decoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "convolutional hierarchical attention network for query focused 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)