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 "exploration on grounded word embedding matching words and images with image enhanced skip gram model"):
  Abstract: Word embedding is designed to represent the semantic meaning of a word with low dimensional vectors. The state-of-the-art methods of learning word embeddings (word2vec and GloVe) only use the word co-occurrence information. The learned embeddings are real number vectors, which are obscure to human. In this paper, we propose an Image-Enhanced Skip-Gram Model to learn grounded word embeddings by representing the word vectors in the same hyper-plane with image vectors. Experiments show that the image vectors and word embeddings learned by our model are highly correlated, which indicates that our model is able to provide a vivid image-based explanation to the word embeddings.

Includes members:
  Member "simnet stepwise image topic merging network for generating detailed and comprehensive image captions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multimodal matching transformer for live commenting")
  Member "zero resource neural machine translation with multi agent communication game":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved zero shot neural machine translation via ignoring spurious correlations")
  Member "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review driven multi label music style classification by exploiting style correlations")
  Member "label embedding network learning label representation for soft training of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchy based image embeddings for semantic image retrieval")
  Member "query and output generating words by querying distributed word representations for paraphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "controllable paraphrase generation with a syntactic exemplar")
  Member "image pivoting for learning multilingual multimodal representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mule multimodal universal language embedding")
  Member "exploring models and data for image 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 "learning deep structure preserving image text embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")

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)