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 "watch it twice video captioning with a refocused video encoder"):
  Abstract: With the rapid growth of video data and the increasing demands of various applications such as intelligent video search and assistance toward visually-impaired people, video captioning task has received a lot of attention recently in computer vision and natural language processing fields. The state-of-the-art video captioning methods focus more on encoding the temporal information, while lack of effective ways to remove irrelevant temporal information and also neglecting the spatial details. However, the current RNN encoding module in single time order can be influenced by the irrelevant temporal information, especially the irrelevant temporal information is at the beginning of the encoding. In addition, neglecting spatial information will lead to the relationship confusion of the words and detailed loss. Therefore, in this paper, we propose a novel recurrent video encoding method and a novel visual spatial feature for the video captioning task. The recurrent encoding module encodes the video twice with the predicted key frame to avoid the irrelevant temporal information often occurring at the beginning and the end of a video. The novel spatial features represent the spatial information in different regions of a video and enrich the details of a caption. Experiments on two benchmark datasets show superior performance of the proposed method.

Includes members:
  Member "unpaired image captioning via scene graph alignments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to collocate neural modules for image captioning")
  Member "scene graph generation with external knowledge and image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object centric image generation from layouts")
  Member "less is more picking informative frames for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "watch listen and describe globally and locally aligned cross modal attentions for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal dense video captioning")
  Member "reconstruction network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "unpaired image captioning by language pivoting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic correlation promoted shape variant context for segmentation")
  Member "end to end video captioning with multitask reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory attended recurrent network for video captioning")
  Member "exploring visual relationship for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)