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 does a car ssette tape tell"):
  Abstract: Captioning has attracted much attention in image and video understanding while little work examines audio captioning. This paper contributes a manually-annotated dataset on car scene, in extension to a previously published hospital audio captioning dataset. An encoder-decoder model with pretrained word embeddings and additional sentence loss is proposed. This current model can accelerate the training process and generate semantically correct but unseen unique sentences. We test the model on the current car dataset, previous Hospital Dataset and the Joint Dataset, indicating its generalization capability across different scenes. Further, we make an effort to provide a better objective evaluation metric, namely the BERT similarity score. It compares the semantic-level similarity and compensates for drawbacks of N-gram based metrics like BLEU, namely high scores for word-similar sentences. This new metric demonstrates higher correlation with human evaluation. However, though detailed audio captions can now be automatically generated, human annotations still outperform model captions in many aspects.

Includes members:
  Member "audio caption listen and tell":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clotho an audio captioning dataset")
  Member "universal dependency parsing from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "unsupervised learning of sentence embeddings using compositional n gram features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "learned in translation contextualized word vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "automated audio captioning with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "audio caption listen and tell")
  Member "weakly supervised dense video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi modal dense video captioning")

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.SD(Sound)