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: "domain adversarial training for accented speech recognition"
  Abstract: In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source domain data ("standard" accent) and unlabeled target domain data (with heavy accents), we augment the learning objective for a Kaldi TDNN network with a domain adversarial training (DAT) objective to encourage the model to learn accent-invariant features. In experiments with three Mandarin accents, we show that DAT yields up to 7.45% relative character error rate reduction when we do not have transcriptions of the accented speech, compared with the baseline trained on standard accent data only. We also find a benefit from DAT when used in combination with training from automatic transcriptions on the accented data. Furthermore, we find that DAT is superior to multi-task learning for accented speech recognition.

Connected through hyperedge HE-1 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual neural machine translation with knowledge distillation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "massively multilingual adversarial speech recognition"
    - "a survey of domain adaptation for neural machine translation"
    - "translating pro drop languages with reconstruction models"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "attentive adversarial learning for domain invariant training"):
  with members:
    - "conditional teacher student learning"
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "attention is all you need"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "a new gan based end to end tts training algorithm"):
  with members:
    - "clarinet parallel wave generation in end to end text to speech"
    - "close to human quality tts with transformer"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"
    - "attention is all you need"
    - "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
    - "tacotron towards end to end speech synthesis"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "massively multilingual adversarial speech recognition"):
  with members:
    - "transfer learning of language independent end to end asr with language model fusion"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "listen attend and spell"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "attention based models for speech recognition"

Connected through hyperedge HE-5 (induced by source paper "aipnet generative adversarial pre training of accent invariant networks for end to end speech recognition"):
  with members:
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "invariant representations for noisy speech recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "adversarial training for multilingual acoustic modeling"):
  with members:
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "towards language universal end to end speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-7 (induced by source paper "to reverse the gradient or not an empirical comparison of adversarial and multi task learning in speech recognition"):
  with members:
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "english conversational telephone speech recognition by humans and machines"
    - "letter based speech recognition with gated convnets"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an overview of multi task learning in deep neural networks"
    - "fader networks manipulating images by sliding attributes"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"

Connected through hyperedge HE-8 (induced by source paper "noise adaptive speech enhancement using domain adversarial training"):
  with members:
    - "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"
    - "adversarial discriminative domain adaptation"
    - "tacotron towards end to end speech synthesis"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "speech enhancement in multiple noise conditions using deep neural 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)