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: "the zero resource speech challenge 2017"
  Abstract: We describe a new challenge aimed at discovering subword and word units from raw speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It aims at constructing systems that generalize across languages and adapt to new speakers. The design features and evaluation metrics of the challenge are presented and the results of seventeen models are discussed.

Connected through hyperedge HE-1 (induced by source paper "learning robust and multilingual speech representations"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "on variational bounds of mutual information"
    - "self training for end to end speech recognition"
    - "libri light a benchmark for asr with limited or no supervision"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (induced by source paper "speech to speech translation between untranscribed unknown languages"):
  with members:
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "the zero resource speech challenge 2019 tts without t"
    - "direct speech to speech translation with a sequence to sequence model"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "sequence to sequence models can directly translate foreign speech"

Connected through hyperedge HE-6 (induced by source paper "the zero resource speech challenge 2019 tts without t"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "close to human quality tts with transformer"
    - "bayesian models for unit discovery on a very low resource language"
    - "voice impersonation using generative adversarial networks"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"

Connected through hyperedge HE-7 (induced by source paper "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "neural discrete representation learning"
    - "full resolution image compression with recurrent neural networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-8 (induced by source paper "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "listening while speaking speech chain by deep learning"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "neural discrete representation learning"

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)