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 "non autoregressive video captioning with iterative refinement"):
  Abstract: Existing state-of-the-art autoregressive video captioning methods (ARVC) generate captions sequentially, which leads to low inference efficiency. Moreover, the word-by-word generation process does not fit human intuition of comprehending video contents (i.e., first capturing the salient visual information and then generating well-organized descriptions), resulting in unsatisfied caption diversity. In order to press close to the human manner of comprehending video contents and writing captions, this paper proposes a non-autoregressive video captioning (NAVC) model with iterative refinement. We then further propose to exploit external auxiliary scoring information to assist the iterative refinement process, which can help the model focus on the inappropriate words more accurately. Experimental results on two mainstream benchmarks, i.e., MSVD and MSR-VTT, show that our proposed method generates more felicitous and diverse captions with a generally faster decoding speed, at the cost of up to 5\% caption quality compared with the autoregressive counterpart. In particular, the proposal of using auxiliary scoring information not only improves non-autoregressive performance by a large margin, but is also beneficial for the caption diversity.

Includes members:
  Member "non autoregressive machine translation with auxiliary regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parallel machine translation with disentangled context transformer")
  Member "temporal deformable convolutional encoder decoder networks for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vision and language from visual perception to content creation")
  Member "mask predict parallel decoding of conditional masked language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blank language models")
  Member "memory attended recurrent network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal dense video captioning")
  Member "a neural compositional paradigm for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "deterministic non autoregressive neural sequence modeling by iterative refinement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non autoregressive neural dialogue generation")
  Member "non autoregressive neural machine translation with enhanced decoder input":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pay less attention with lightweight and dynamic convolutions")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)