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: "an unsupervised autoregressive model for speech representation learning"
  Abstract: This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any phonetic or word boundary labels, allowing the model to benefit from large quantities of unlabeled data. Speech representations learned by our model significantly improve performance on both phone classification and speaker verification over the surface features and other supervised and unsupervised approaches. Further analysis shows that different levels of speech information are captured by our model at different layers. In particular, the lower layers tend to be more discriminative for speakers, while the upper layers provide more phonetic content.

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 "effectiveness of self supervised pre training for speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "acoustically grounded word embeddings for improved acoustics to word speech recognition"
    - "cloze driven pretraining of self attention networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "vq wav2vec self supervised learning of discrete speech representations"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "unsupervised pre training of image features on non curated data"

Connected through hyperedge HE-4 (induced by source paper "transfer learning from audio visual grounding to speech recognition"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "deep contextualized word representations"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "unsupervised textual grounding linking words to image concepts"
    - "jointly discovering visual objects and spoken words from raw sensory input"

Connected through hyperedge HE-5 (induced by source paper "improving transformer based speech recognition using unsupervised pre training"):
  with members:
    - "pre trained language model representations for language generation"
    - "a comparative study on transformer vs rnn in speech applications"
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "wav2vec unsupervised pre training for speech recognition"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "roberta a robustly optimized bert pretraining approach"
    - "improving speech emotion recognition via transformer based predictive coding through transfer learning"

Connected through hyperedge HE-6 (induced by source paper "towards transfer learning for end to end speech synthesis from deep pre trained language models"):
  with members:
    - "feature reinforcement with word embedding and parsing information in neural tts"
    - "waveglow a flow based generative network for speech synthesis"
    - "clarinet parallel wave generation in end to end text to speech"
    - "deep contextualized word representations"
    - "neural speech synthesis with transformer network"
    - "universal language model fine tuning for text classification"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "towards unsupervised speech recognition and synthesis with quantized speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "vq wav2vec self supervised learning of discrete speech representations"
    - "wav2vec unsupervised pre training for speech recognition"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "representation learning with contrastive predictive coding"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "neural discrete representation learning"

Connected through hyperedge HE-8 (induced by source paper "speech xlnet unsupervised acoustic model pretraining for self attention networks"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "wav2vec unsupervised pre training for speech recognition"
    - "a survey of recent dnn architectures on the timit phone recognition task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

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)