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: "cycle consistency training for end to end speech recognition"
  Abstract: This paper presents a method to train end-to-end automatic speech recognition (ASR) models using unpaired data. Although the end-to-end approach can eliminate the need for expert knowledge such as pronunciation dictionaries to build ASR systems, it still requires a large amount of paired data, i.e., speech utterances and their transcriptions. Cycle-consistency losses have been recently proposed as a way to mitigate the problem of limited paired data. These approaches compose a reverse operation with a given transformation, e.g., text-to-speech (TTS) with ASR, to build a loss that only requires unsupervised data, speech in this example. Applying cycle consistency to ASR models is not trivial since fundamental information, such as speaker traits, are lost in the intermediate text bottleneck. To solve this problem, this work presents a loss that is based on the speech encoder state sequence instead of the raw speech signal. This is achieved by training a Text-To-Encoder model and defining a loss based on the encoder reconstruction error. Experimental results on the LibriSpeech corpus show that the proposed cycle-consistency training reduced the word error rate by 14.7% from an initial model trained with 100-hour paired data, using an additional 360 hours of audio data without transcriptions. We also investigate the use of text-only data mainly for language modeling to further improve the performance in the unpaired data training scenario.

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"
    - "end to end feedback loss in speech chain framework via straight through estimator"

Connected through hyperedge HE-2 (induced by source paper "self training for end to end speech recognition"):
  with members:
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "lessons from building acoustic models with a million hours of speech"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "adversarial training of end to end speech recognition using a criticizing language model"
    - "fully convolutional speech recognition"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-3 (induced by source paper "phasebook and friends leveraging discrete representations for source separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "the conversation deep audio visual speech enhancement"
    - "sdr half baked or well done"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "deep clustering discriminative embeddings for segmentation and separation"

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)