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: "unspeech unsupervised speech context embeddings"
  Abstract: We introduce "Unspeech" embeddings, which are based on unsupervised learning of context feature representations of spoken language. The embeddings were trained on up to 9500 hours of crawled English speech data without transcriptions or speaker information, by using a straightforward learning objective based on context and non-context discrimination with negative sampling. We use a Siamese convolutional neural network architecture to train Unspeech embeddings and evaluate them on speaker comparison, utterance clustering and as a context feature in TDNN-HMM acoustic models trained on TED-LIUM, comparing it to i-vector baselines. Particularly decoding out-of-domain speech data from the recently released Common Voice corpus shows consistent WER reductions. We release our source code and pre-trained Unspeech models under a permissive open source license.

Connected through hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "language learning using speech to image retrieval"
    - "towards visually grounded sub word speech unit discovery"
    - "the zero resource speech challenge 2019 tts without t"
    - "word recognition competition and activation in a model of visually grounded speech"

Connected through hyperedge HE-2 (induced by source paper "an unsupervised autoregressive model for speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "dissecting contextual word embeddings architecture and representation"
    - "universal language model fine tuning for text classification"
    - "on training recurrent networks with truncated backpropagation through time in speech recognition"

Connected through hyperedge HE-3 (induced by source paper "contextual joint factor acoustic embeddings"):
  with members:
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning latent representations for speech generation and transformation"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "learning word embeddings from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "context encoders feature learning by inpainting"
    - "sequence to sequence rnns for text summarization"

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)