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: "letter based speech recognition with gated convnets"
  Abstract: In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires -- only audio transcription for training -- no alignment annotations, nor any forced alignment step is needed. At inference, our decoder takes only a word list and a language model, and is fed with letter scores from the -- acoustic model -- no phonetic word lexicon is needed. Key ingredients for the acoustic model are Gated Linear Units and high dropout. We show near state-of-the-art results in word error rate on the LibriSpeech corpus using log-mel filterbanks, both on the "clean" and "other" configurations.

Connected through hyperedge HE-1 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "attention is all you need"
    - "improving end to end speech recognition with policy learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "cif continuous integrate and fire for end to end speech recognition"):
  with members:
    - "transformers with convolutional context for asr"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "espnet end to end speech processing toolkit"
    - "wav2letter the fastest open source speech recognition system"
    - "an online attention based model for speech recognition"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-3 (induced by source paper "lead2gold towards exploiting the full potential of noisy transcriptions for speech recognition"):
  with members:
    - "active and semi supervised learning in asr benefits on the acoustic and language models"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "a fully differentiable beam search decoder"
    - "wav2letter the fastest open source speech recognition system"
    - "label noise robust generative adversarial networks"
    - "improving lstm ctc based asr performance in domains with limited training data"
    - "wav2letter an end to end convnet based speech recognition system"
    - "learning deep networks from noisy labels with dropout regularization"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "convergence of value aggregation for imitation learning"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-6 (induced by source paper "improved training of end to end attention models for speech recognition"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "state of the art speech recognition with sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

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"
    - "domain adversarial training for accented speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "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 "exploring end to end techniques for low resource speech recognition"):
  with members:
    - "sequence based multi lingual low resource speech recognition"
    - "improved regularization techniques for end to end speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "deep speech scaling up end to end speech recognition"
    - "sequence transduction with recurrent 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)