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: "attention based wav2text with feature transfer learning"
  Abstract: Conventional automatic speech recognition (ASR) typically performs multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units. But, it is widely known that information loss in the earlier stage can propagate through the later stages. After the resurgence of deep learning, interest has emerged in the possibility of developing a purely end-to-end ASR system from the raw waveform to the transcription without any predefined alignments and hand-engineered models. However, the successful attempts in end-to-end architecture still used spectral-based features, while the successful attempts in using raw waveform were still based on the hybrid deep neural network - Hidden Markov model (DNN-HMM) framework. In this paper, we construct the first end-to-end attention-based encoder-decoder model to process directly from raw speech waveform to the text transcription. We called the model as "Attention-based Wav2Text". To assist the training process of the end-to-end model, we propose to utilize a feature transfer learning. Experimental results also reveal that the proposed Attention-based Wav2Text model directly with raw waveform could achieve a better result in comparison with the attentional encoder-decoder model trained on standard front-end filterbank features.

Connected through hyperedge HE-1 (induced by source paper "end to end speech recognition from the raw waveform"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "the microsoft 2017 conversational speech recognition system"
    - "improving end to end speech recognition with policy learning"
    - "learning filterbanks from raw speech for phone recognition"
    - "sequence to sequence asr optimization via reinforcement learning"
    - "wav2letter an end to end convnet based speech recognition system"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-2 (induced by source paper "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"):
  with members:
    - "machine speech chain with one shot speaker adaptation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-3 (induced by source paper "end to end feedback loss in speech chain framework via straight through estimator"):
  with members:
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "machine speech chain with one shot speaker adaptation"):
  with members:
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "deep speaker an end to end neural speaker embedding system"
    - "perceptual losses for real time style transfer and super resolution"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "wavenet a generative model for raw audio"
    - "end to end attention based large vocabulary speech recognition"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-5 (induced by source paper "sequence level knowledge distillation for model compression of attention based sequence to sequence speech recognition"):
  with members:
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "sequence level knowledge distillation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-6 (induced by source paper "sequence to sequence asr optimization via reinforcement learning"):
  with members:
    - "optimizing expected word error rate via sampling for speech recognition"
    - "deep reinforcement learning for dialogue generation"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "sequence level training with recurrent neural networks"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-7 (induced by source paper "learning filterbanks from raw speech for phone recognition"):
  with members:
    - "wav2letter an end to end convnet based speech recognition system"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "wavenet a generative model for raw audio"
    - "achieving human parity in conversational speech recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "network in network"
    - "end to end phoneme sequence recognition using convolutional 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)