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 "for your eyes only learning to summarize first person videos"):
  Abstract: With the increasing amount of video data, it is desirable to highlight or summarize the videos of interest for viewing, search, or storage purposes. However, existing summarization approaches are typically trained from third-person videos, which cannot generalize to highlight the first-person ones. By advancing deep learning techniques, we propose a unique network architecture for transferring spatiotemporal information across video domains, which jointly solves metric-learning based feature embedding and keyframe selection via Bidirectional Long Short-Term Memory (BiLSTM). A practical semi-supervised learning setting is considered, i.e., only fully annotated third-person videos, unlabeled first-person videos, and a small amount of annotated first-person ones are required to train our proposed model. Qualitative and quantitative evaluations are performed in our experiments, which confirm that our model performs favorably against baseline and state-of-the-art approaches on first-person video summarization.

Includes members:
  Member "a general framework for edited video and raw video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning for task driven video summarization")
  Member "video summarization with attention based encoder decoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning for task driven video summarization")
  Member "video summarization with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "video storytelling textual summaries for events")
  Member "video2gif automatic generation of animated gifs from video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "talk2nav long range vision and language navigation with dual attention and spatial memory")
  Member "predicting important objects for egocentric video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "learning spatiotemporal features with 3d convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "deep metric learning using triplet network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "opengan open set generative adversarial networks")
  Member "two stream convolutional networks for action recognition in videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)