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 "what is the role of recurrent neural networks rnns in an image caption generator"):
  Abstract: In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary `generation' component. This view suggests that the image features should be `injected' into the RNN. This is in fact the dominant view in the literature. Alternatively, the RNN can instead be viewed as only encoding the previously generated words. This view suggests that the RNN should only be used to encode linguistic features and that only the final representation should be `merged' with the image features at a later stage. This paper compares these two architectures. We find that, in general, late merging outperforms injection, suggesting that RNNs are better viewed as encoders, rather than generators.

Includes members:
  Member "where to put the image in an image caption generator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures")
  Member "image captioning with semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "knowing when to look adaptive attention via a visual sentinel for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "unsupervised pretraining for sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "encode review and decode reviewer module for caption generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mrrc multiple role representation crossover interpretation for image captioning with r cnn feature distribution composition fdc")
  Member "image caption generation with text conditional semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "vqa visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "show attend and tell neural image caption generation with visual attention":
    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.CL(Computation and Language)