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 "video captioning with text based dynamic attention and step by step learning"):
  Abstract: Automatically describing video content with natural language has been attracting much attention in CV and NLP communities. Most existing methods predict one word at a time, and by feeding the last generated word back as input at the next time, while the other generated words are not fully exploited. Furthermore, traditional methods optimize the model using all the training samples in each epoch without considering their learning situations, which leads to a lot of unnecessary training and can not target the difficult samples. To address these issues, we propose a text-based dynamic attention model named TDAM, which imposes a dynamic attention mechanism on all the generated words with the motivation to improve the context semantic information and enhance the overall control of the whole sentence. Moreover, the text-based dynamic attention mechanism and the visual attention mechanism are linked together to focus on the important words. They can benefit from each other during training. Accordingly, the model is trained through two steps: "starting from scratch" and "checking for gaps". The former uses all the samples to optimize the model, while the latter only trains for samples with poor control. Experimental results on the popular datasets MSVD and MSR-VTT demonstrate that our non-ensemble model outperforms the state-of-the-art video captioning benchmarks.

Includes members:
  Member "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "memory attended recurrent network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal dense video captioning")
  Member "object aware aggregation with bidirectional temporal graph for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "reconstruction network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "end to end video captioning with multitask reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory attended recurrent network for video captioning")
  Member "saliency guided video classification via adaptively weighted learning":
    also connected through other hyperedges:
      (none)
  Member "hierarchical lstm with adjusted temporal attention for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical memory decoding for video captioning")
  Member "hierarchical boundary aware neural encoder for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi modal dense video captioning")

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)