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: "discriminative acoustic word embeddings recurrent neural network based approaches"
  Abstract: Acoustic word embeddings --- fixed-dimensional vector representations of variable-length spoken word segments --- have begun to be considered for tasks such as speech recognition and query-by-example search. Such embeddings can be learned discriminatively so that they are similar for speech segments corresponding to the same word, while being dissimilar for segments corresponding to different words. Recent work has found that acoustic word embeddings can outperform dynamic time warping on query-by-example search and related word discrimination tasks. However, the space of embedding models and training approaches is still relatively unexplored. In this paper we present new discriminative embedding models based on recurrent neural networks (RNNs). We consider training losses that have been successful in prior work, in particular a cross entropy loss for word classification and a contrastive loss that explicitly aims to separate same-word and different-word pairs in a "Siamese network" training setting. We find that both classifier-based and Siamese RNN embeddings improve over previously reported results on a word discrimination task, with Siamese RNNs outperforming classification models. In addition, we present analyses of the learned embeddings and the effects of variables such as dimensionality and network structure.

Connected through hyperedge HE-1 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages"):
  with members:
    - "learned in speech recognition contextual acoustic word embeddings"
    - "audio linguistic embeddings for spoken sentences"
    - "bayesian subspace hidden markov model for acoustic unit discovery"
    - "massively multilingual adversarial speech recognition"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "truly unsupervised acoustic word embeddings using weak top down constraints in encoder decoder models"

Connected through hyperedge HE-2 (induced by source paper "improving automated segmentation of radio shows with audio embeddings"):
  with members:
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "sound word2vec learning word representations grounded in sounds"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"
    - "advances in domain independent linear text segmentation"

Connected through hyperedge HE-3 (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"
    - "unspeech unsupervised speech context embeddings"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "decoupled learning for conditional adversarial networks"
    - "improved audio embeddings by adjacency based clustering with applications in spoken term detection"
    - "voice impersonation using generative adversarial networks"
    - "unsupervised learning of semantic audio representations"
    - "tacotron towards end to end speech synthesis"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (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"
    - "learning word embeddings from speech"

Connected through hyperedge HE-6 (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"
    - "learning word embeddings from speech"
    - "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-7 (induced by source paper "semantic query by example speech search using visual grounding"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "low resource speech to text translation"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "visually grounded cross lingual keyword spotting in speech"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"

Connected through hyperedge HE-8 (induced by source paper "acoustically grounded word embeddings for improved acoustics to word speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "end to end asr free keyword search from speech"

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)