human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Question: Please tell me which class the center node 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)