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: "vq wav2vec self supervised learning of discrete speech representations"
  Abstract: We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations. Discretization enables the direct application of algorithms from the NLP community which require discrete inputs. Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition.

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 "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"
    - "an unsupervised autoregressive model for speech representation learning"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"

Connected through hyperedge HE-3 (induced by source paper "libri light a benchmark for asr with limited or no supervision"):
  with members:
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"
    - "wav2vec unsupervised pre training for speech recognition"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "towards unsupervised speech recognition and synthesis with quantized speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "an unsupervised autoregressive model for speech representation learning"
    - "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"

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)