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 "netizen style commenting on fashion photos dataset and diversity measures"):
  Abstract: Recently, deep neural network models have achieved promising results in image captioning task. Yet, "vanilla" sentences, only describing shallow appearances (e.g., types, colors), generated by current works are not satisfied netizen style resulting in lacking engagements, contexts, and user intentions. To tackle this problem, we propose Netizen Style Commenting (NSC), to automatically generate characteristic comments to a user-contributed fashion photo. We are devoted to modulating the comments in a vivid "netizen" style which reflects the culture in a designated social community and hopes to facilitate more engagement with users. In this work, we design a novel framework that consists of three major components: (1) We construct a large-scale clothing dataset named NetiLook, which contains 300K posts (photos) with 5M comments to discover netizen-style comments. (2) We propose three unique measures to estimate the diversity of comments. (3) We bring diversity by marrying topic models with neural networks to make up the insufficiency of conventional image captioning works. Experimenting over Flickr30k and our NetiLook datasets, we demonstrate our proposed approaches benefit fashion photo commenting and improve image captioning tasks both in accuracy and diversity.

Includes members:
  Member "generating high quality and informative conversation responses with sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation")
  Member "text guided attention model for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streamlined dense video captioning")
  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 "smart reply automated response suggestion for email":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network")
  Member "diverse beam search decoding diverse solutions from neural sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non autoregressive neural dialogue generation")
  Member "captioning images with diverse objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unshuffling data for improved generalization")
  Member "image captioning with deep bidirectional lstms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "bidirectional lstm crf models for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dense dilated convolutions merging network for land cover classification")

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)