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: "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
  Abstract: In this paper, we present TED-LIUM release 3 corpus (TED-LIUM 3 is available on https://lium.univ-lemans.fr/ted-lium3/) dedicated to speech recognition in English, which multiplies the available data to train acoustic models in comparison with TED-LIUM 2, by a factor of more than two. We present the recent development on Automatic Speech Recognition (ASR) systems in comparison with the two previous releases of the TED-LIUM Corpus from 2012 and 2014. We demonstrate that, passing from 207 to 452 h of transcribed speech training data is really more useful for end-to-end ASR systems than for HMM-based state-of-the-art ones. This is the case even if the HMM-based ASR system still outperforms the end-to-end ASR system when the size of audio training data is 452 h, with a Word Error Rate (WER) of 6.7% and 13.7%, respectively. Finally, we propose two repartitions of the TED-LIUM release 3 corpus: the legacy repartition that is the same as that existing in release 2, and a new repartition, calibrated and designed to make experiments on speaker adaptation. Similar to the two first releases, TED-LIUM 3 corpus will be freely available for the research community.

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 "transformer based language modeling and decoding for conversational speech recognition"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-3 (induced by source paper "a comparative study on transformer vs rnn in speech applications"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "neural speech synthesis with transformer network"
    - "end to end speech recognition with word based rnn language models"

Connected through hyperedge HE-4 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "on the robustness of speech emotion recognition for human robot interaction with deep neural networks"
    - "multi attention recurrent network for human communication comprehension"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities"
    - "reusing neural speech representations for auditory emotion recognition"
    - "cnn lstm architecture for speech emotion recognition with data augmentation"

Connected through hyperedge HE-5 (induced by source paper "very deep self attention networks for end to end speech recognition"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentional acoustic models"
    - "training deeper neural machine translation models with transparent attention"
    - "improved training of end to end attention models for speech recognition"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "very deep convolutional networks for end to end speech recognition"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "on trac consortium end to end speech translation systems for the iwslt 2019 shared task"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "towards unsupervised speech to text translation"
    - "espnet end to end speech processing toolkit"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "leveraging weakly supervised data to improve end to end speech to text translation"
    - "sequence to sequence models can directly translate foreign speech"

Connected through hyperedge HE-7 (induced by source paper "ims speech a speech to text tool"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "the capio 2017 conversational speech recognition system"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "open source automatic speech recognition for german"
    - "improved training of end to end attention models for speech recognition"
    - "deep recurrent neural networks for acoustic modelling"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "l2rs a learning to rescore mechanism for automatic speech recognition"):
  with members:
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "on a topic model for sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "finding consensus in speech recognition word error minimization and other applications of confusion networks"

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)