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: "wav2vec unsupervised pre training for speech recognition"
  Abstract: We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized via a noise contrastive binary classification task. Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available. Our approach achieves 2.43% WER on the nov92 test set. This outperforms Deep Speech 2, the best reported character-based system in the literature while using two orders of magnitude less labeled training data.

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 "assessment modeling fundamental pre training tasks for interactive educational systems"):
  with members:
    - "ednet a large scale hierarchical dataset in education"
    - "quesnet a unified representation for heterogeneous test questions"
    - "creating a neural pedagogical agent by jointly learning to review and assess"
    - "a comprehensive study of imagenet pre training for historical document image analysis"
    - "exploiting cognitive structure for adaptive learning"
    - "a self attentive model for knowledge tracing"
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "selfie self supervised pretraining for image embedding"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "unsupervised data augmentation for consistency training"
    - "revisiting self supervised visual representation learning"
    - "deep contextualized word representations"
    - "autoaugment learning augmentation policies from data"
    - "unsupervised representation learning by predicting image rotations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "training asr models by generation of contextual information"):
  with members:
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "lessons from building acoustic models with a million hours of speech"
    - "roberta a robustly optimized bert pretraining approach"
    - "hierarchical neural story generation"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"

Connected through hyperedge HE-7 (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"
    - "an unsupervised autoregressive model for speech representation learning"
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "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-8 (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"
    - "vq wav2vec self supervised learning of discrete speech representations"
    - "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"

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)