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: "end to end feedback loss in speech chain framework via straight through estimator"
  Abstract: The speech chain mechanism integrates automatic speech recognition (ASR) and text-to-speech synthesis (TTS) modules into a single cycle during training. In our previous work, we applied a speech chain mechanism as a semi-supervised learning. It provides the ability for ASR and TTS to assist each other when they receive unpaired data and let them infer the missing pair and optimize the model with reconstruction loss. If we only have speech without transcription, ASR generates the most likely transcription from the speech data, and then TTS uses the generated transcription to reconstruct the original speech features. However, in previous papers, we just limited our back-propagation to the closest module, which is the TTS part. One reason is that back-propagating the error through the ASR is challenging due to the output of the ASR are discrete tokens, creating non-differentiability between the TTS and ASR. In this paper, we address this problem and describe how to thoroughly train a speech chain end-to-end for reconstruction loss using a straight-through estimator (ST). Experimental results revealed that, with sampling from ST-Gumbel-Softmax, we were able to update ASR parameters and improve the ASR performances by 11\% relative CER reduction compared to the baseline.

Connected through hyperedge HE-1 (induced by source paper "self supervised speech recognition via local prior matching"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "parallel neural text to speech"
    - "libri light a benchmark for asr with limited or no supervision"
    - "lessons from building acoustic models with a million hours of speech"
    - "a fully differentiable beam search decoder"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cycle consistency training for end to end speech recognition"

Connected through hyperedge HE-2 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning"):
  with members:
    - "deep audio visual speech recognition"
    - "towards robust neural machine translation"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "turbo learning for captionbot and drawingbot"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "end to end audiovisual fusion with lstms"

Connected through hyperedge HE-3 (induced by source paper "speech to speech translation between untranscribed unknown languages"):
  with members:
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "the zero resource speech challenge 2019 tts without t"
    - "direct speech to speech translation with a sequence to sequence model"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "sequence to sequence models can directly translate foreign speech"
    - "listening while speaking speech chain by deep learning"

Connected through hyperedge HE-4 (induced by source paper "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "neural machine translation with gumbel greedy decoding"
    - "listening while speaking speech chain by deep learning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "generating synthetic audio data for attention based speech recognition systems"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "libritts a corpus derived from librispeech for text to speech"
    - "cumulative adaptation for blstm acoustic models"
    - "back translation style data augmentation for end to end asr"
    - "machine speech chain with one shot speaker adaptation"
    - "training neural speech recognition systems with synthetic speech augmentation"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-6 (induced by source paper "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "machine speech chain with one shot speaker adaptation"
    - "listening while speaking speech chain by deep learning"
    - "the zero resource speech challenge 2017"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "neural discrete representation learning"

Connected through hyperedge HE-7 (induced by source paper "self supervised speaker embeddings"):
  with members:
    - "mtgan speaker verification through multitasking triplet generative adversarial networks"
    - "boosting self supervised learning via knowledge transfer"
    - "self supervised learning of a facial attribute embedding from video"
    - "sample efficient adaptive text to speech"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "voxceleb2 deep speaker recognition"
    - "deep speaker an end to end neural speaker embedding system"
    - "time contrastive networks self supervised learning from video"

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)