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: "leveraging weakly supervised data to improve end to end speech to text translation"
  Abstract: End-to-end Speech Translation (ST) models have many potential advantages when compared to the cascade of Automatic Speech Recognition (ASR) and text Machine Translation (MT) models, including lowered inference latency and the avoidance of error compounding. However, the quality of end-to-end ST is often limited by a paucity of training data, since it is difficult to collect large parallel corpora of speech and translated transcript pairs. Previous studies have proposed the use of pre-trained components and multi-task learning in order to benefit from weakly supervised training data, such as speech-to-transcript or text-to-foreign-text pairs. In this paper, we demonstrate that using pre-trained MT or text-to-speech (TTS) synthesis models to convert weakly supervised data into speech-to-translation pairs for ST training can be more effective than multi-task learning. Furthermore, we demonstrate that a high quality end-to-end ST model can be trained using only weakly supervised datasets, and that synthetic data sourced from unlabeled monolingual text or speech can be used to improve performance. Finally, we discuss methods for avoiding overfitting to synthetic speech with a quantitative ablation study.

Connected through hyperedge HE-1 (induced by source paper "multilingual end to end speech translation"):
  with members:
    - "end to end speech translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "tied multitask learning for neural speech translation"
    - "transfer learning of language independent end to end asr with language model fusion"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence based multi lingual low resource speech recognition"

Connected through hyperedge HE-2 (induced by source paper "one to many multilingual end to end speech translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "end to end speech translation with knowledge distillation"
    - "achieving human parity on automatic chinese to english news translation"
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "deep neural machine translation with weakly recurrent units"
    - "structured based curriculum learning for end to end english japanese speech translation"

Connected through hyperedge HE-3 (induced by source paper "a comparative study on end to end speech to text translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "on using 2d sequence to sequence models for speech recognition"
    - "end to end speech translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised speech to text translation"
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"

Connected through hyperedge HE-4 (induced by source paper "on using specaugment for end to end speech translation"):
  with members:
    - "a comparative study on end to end speech to text translation"
    - "on using 2d sequence to sequence models for speech recognition"
    - "end to end speech translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised speech to text translation"
    - "end to end automatic speech translation of audiobooks"
    - "improved training of end to end attention models for speech recognition"
    - "sequence to sequence models can directly translate foreign speech"

Connected through hyperedge HE-5 (induced by source paper "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"):
  with members:
    - "europarl st a multilingual corpus for speech translation of parliamentary debates"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "massively multilingual neural machine translation"
    - "direct speech to speech translation with a sequence to sequence model"
    - "massively multilingual adversarial speech recognition"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised speech to text translation"

Connected through hyperedge HE-6 (induced by source paper "synchronous speech recognition and speech to text translation with interactive decoding"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "end to end speech translation with knowledge distillation"
    - "synchronous bidirectional inference for neural sequence generation"
    - "tied multitask learning for neural speech translation"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"

Connected through hyperedge HE-7 (induced by source paper "data efficient direct speech to text translation with modality agnostic meta learning"):
  with members:
    - "end to end speech translation with knowledge distillation"
    - "tied multitask learning for neural speech translation"
    - "universal neural machine translation for extremely low resource languages"
    - "end to end automatic speech translation of audiobooks"
    - "meta learning for low resource neural machine translation"
    - "structured based curriculum learning for end to end english japanese speech translation"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "leveraging out of task data for end to end automatic speech translation"):
  with members:
    - "transformers with convolutional context for asr"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "espnet end to end speech processing toolkit"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"

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)