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 "a semantics assisted video captioning model trained with scheduled sampling"):
  Abstract: Given the features of a video, recurrent neural network can be used to automatically generate a caption for the video. Existing methods for video captioning have at least three limitations. First, semantic information has been widely applied to boost the performance of video captioning models, but existing networks often fail to provide meaningful semantic features. Second, Teacher Forcing algorithm is often utilized to optimize video captioning models, but during training and inference, different strategies are applied to guide word generation, which lead to poor performance. Third, current video captioning models are prone to generate relatively short captions, which express video contents inappropriately. Towards resolving these three problems, we make three improvements correspondingly. First of all, we utilize both static spatial features and dynamic spatio-temporal features as input for semantic detection network (SDN) in order to generate meaningful semantic features for videos. Then, we propose a scheduled sampling strategy which gradually transfers the training phase from a teacher guiding manner towards a more self teaching manner. At last, the ordinary logarithm probability loss function is leveraged by sentence length so that short sentence inclination is alleviated. Our model achieves state-of-the-art results on the Youtube2Text dataset and is competitive with the state-of-the-art models on the MSR-VTT dataset.

Includes members:
  Member "videobert a joint model for video and language representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "less is more picking informative frames for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "learning to compose topic aware mixture of experts for zero shot video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "delving deeper into the decoder 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 "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "exploring visual relationship for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "reinforced video captioning with entailment rewards":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "delving deeper into the decoder for video captioning")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)