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 "on architectures for including visual information in neural language models for image description"):
  Abstract: A neural language model can be conditioned into generating descriptions for images by providing visual information apart from the sentence prefix. This visual information can be included into the language model through different points of entry resulting in different neural architectures. We identify four main architectures which we call init-inject, pre-inject, par-inject, and merge. #R##N#We analyse these four architectures and conclude that the best performing one is init-inject, which is when the visual information is injected into the initial state of the recurrent neural network. We confirm this using both automatic evaluation measures and human annotation. #R##N#We then analyse how much influence the images have on each architecture. This is done by measuring how different the output probabilities of a model are when a partial sentence is combined with a completely different image from the one it is meant to be combined with. We find that init-inject tends to quickly become less influenced by the image as more words are generated. A different architecture called merge, which is when the visual information is merged with the recurrent neural network's hidden state vector prior to output, loses visual influence much more slowly, suggesting that it would work better for generating longer sentences. #R##N#We also observe that the merge architecture can have its recurrent neural network pre-trained in a text-only language model (transfer learning) rather than be initialised randomly as usual. This results in even better performance than the other architectures, provided that the source language model is not too good at language modelling or it will overspecialise and be less effective at image description generation. #R##N#Our work opens up new avenues of research in neural architectures, explainable AI, and transfer learning.

Includes members:
  Member "transfer learning from language models to image caption generators better models may not transfer better":
    also connected through other hyperedges:
      (none)
  Member "visual entailment a novel task for fine grained image understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale pretraining for visual dialog a simple state of the art baseline")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "quantifying the amount of visual information used by neural caption generators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "visuallly grounded generation of entailments from premises")
  Member "pre gen metrics predicting caption quality metrics without generating captions":
    also connected through other hyperedges:
      (none)
  Member "object counts bringing explicit detections back into image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visuallly grounded generation of entailments from premises")
  Member "the memad submission to the wmt18 multimodal translation task":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research")
  Member "grounded textual entailment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual entailment a novel task for fine grained image understanding")

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.NE(Neural and Evolutionary Computing)