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 "beyond caption to narrative video captioning with multiple sentences"):
  Abstract: Recent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method without explicitly using video-level features as input.

Includes members:
  Member "jointly modeling embedding and translation to bridge video and language":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "a dataset for movie description":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "using descriptive video services to create a large data source for video annotation research":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the evaluation of intelligent process automation")
  Member "learning to track for spatio temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "actions as moving points")
  Member "video captioning with recurrent networks based on frame and video level features and visual content classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a review of modularization techniques in artificial neural networks")
  Member "aligning books and movies towards story like visual explanations by watching movies and reading books":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "action recognition with trajectory pooled deep convolutional descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "going deeper with convolutions":
    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)