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: "pre training on high resource speech recognition improves low resource speech to text translation"
  Abstract: We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tune its parameters for ST. We demonstrate that our approach is effective by pre-training on 300 hours of English ASR data to improve Spanish-English ST from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data are available. Through an ablation study, we find that the pre-trained encoder (acoustic model) accounts for most of the improvement, despite the fact that the shared language in these tasks is the target language text, not the source language audio. Applying this insight, we show that pre-training on ASR helps ST even when the ASR language differs from both source and target ST languages: pre-training on French ASR also improves Spanish-English ST. Finally, we show that the approach improves performance on a true low-resource task: pre-training on a combination of English ASR and French ASR improves Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1 BLEU.

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"
    - "sequence based multi lingual low resource speech recognition"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

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"
    - "deep neural machine translation with weakly recurrent units"
    - "leveraging weakly supervised data to improve end to end speech to text translation"
    - "structured based curriculum learning for end to end english japanese speech translation"

Connected through hyperedge HE-3 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"

Connected through hyperedge HE-4 (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-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"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-7 (induced by source paper "end to end speech translation with knowledge distillation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "end to end automatic speech translation of audiobooks"
    - "large scale distributed neural network training through online distillation"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "ensemble distillation for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "analyzing asr pretraining for low resource speech to text translation"):
  with members:
    - "fluent translations from disfluent speech in end to end speech translation"
    - "massively multilingual adversarial speech recognition"
    - "tied multitask learning for neural speech translation"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "low resource speech to text translation"
    - "end to end automatic speech translation of audiobooks"
    - "sequence based multi lingual low resource speech recognition"
    - "multilingual bottleneck features for subword modeling in zero resource languages"

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)