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: "learning word embeddings from speech"
  Abstract: In this paper, we propose a novel deep neural network architecture, Sequence-to-Sequence Audio2Vec, for unsupervised learning of fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the segments, and are close to other vectors in the embedding space if their corresponding segments are semantically similar. The design of the proposed model is based on the RNN Encoder-Decoder framework, and borrows the methodology of continuous skip-grams for training. The learned vector representations are evaluated on 13 widely used word similarity benchmarks, and achieved competitive results to that of GloVe. The biggest advantage of the proposed model is its capability of extracting semantic information of audio segments taken directly from raw speech, without relying on any other modalities such as text or images, which are challenging and expensive to collect and annotate.

Connected through hyperedge HE-1 (induced by source paper "unsupervised speech representation learning using wavenet autoencoders"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "disentangled sequential autoencoder"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "learning joint acoustic phonetic word embeddings"):
  with members:
    - "a survey of multi view representation learning"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "sampling matters in deep embedding learning"
    - "learning word like units from joint audio visual analysis"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-3 (induced by source paper "contextual joint factor acoustic embeddings"):
  with members:
    - "unspeech unsupervised speech context embeddings"
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "speech2vec a sequence to sequence framework for learning word embeddings from speech"):
  with members:
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "learning word like units from joint audio visual analysis"
    - "supervised and unsupervised transfer learning for question answering"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "speech based visual question answering"
    - "character composition model with convolutional neural networks for dependency parsing on morphologically rich languages"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"

Connected through hyperedge HE-5 (induced by source paper "unsupervised cross modal alignment of speech and text embedding spaces"):
  with members:
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised machine translation using monolingual corpora only"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"

Connected through hyperedge HE-6 (induced by source paper "towards unsupervised speech to text translation"):
  with members:
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "unsupervised statistical machine translation"
    - "end to end automatic speech translation of audiobooks"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-7 (induced by source paper "on the difficulty of a distributional semantics of spoken language"):
  with members:
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "learning word like units from joint audio visual analysis"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "representations of language in a model of visually grounded speech signal"
    - "neural discrete representation learning"
    - "self adaptive hierarchical sentence model"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "supervised and unsupervised transfer learning for question answering"):
  with members:
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "aspect augmented adversarial networks for domain adaptation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "neural domain adaptation for biomedical question answering"
    - "query based attention cnn for text similarity map"
    - "newsqa a machine comprehension dataset"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
    - "squad 100 000 questions for machine comprehension of text"

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.CL(Computation and Language)