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: "structured based curriculum learning for end to end english japanese speech translation"
  Abstract: Sequence-to-sequence attentional-based neural network architectures have been shown to provide a powerful model for machine translation and speech recognition. Recently, several works have attempted to extend the models for end-to-end speech translation task. However, the usefulness of these models were only investigated on language pairs with similar syntax and word order (e.g., English-French or English-Spanish). In this work, we focus on end-to-end speech translation tasks on syntactically distant language pairs (e.g., English-Japanese) that require distant word reordering. #R##N#To guide the encoder-decoder attentional model to learn this difficult problem, we propose a structured-based curriculum learning strategy. #R##N#Unlike conventional curriculum learning that gradually emphasizes difficult data examples, we formalize learning strategies from easier network structures to more difficult network structures. Here, we start the training with end-to-end encoder-decoder for speech recognition or text-based machine translation task then gradually move to end-to-end speech translation task. The experiment results show that the proposed approach could provide significant improvements in comparison with the one without curriculum learning.

Connected through hyperedge HE-1 (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"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-2 (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-3 (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"
    - "leveraging weakly supervised data to improve end to end speech to text translation"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "instance based model adaptation for direct speech translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "non parametric adaptation for neural machine translation"
    - "end to end speech translation with knowledge distillation"
    - "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"
    - "the ustc nel speech translation system at iwslt 2018"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-5 (induced by source paper "bridging the gap between pre training and fine tuning for 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"
    - "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"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-6 (induced by source paper "attention passing models for robust and data efficient end to end speech translation"):
  with members:
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence to sequence models can directly translate foreign speech"
    - "improving lexical choice in neural machine translation"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "very deep convolutional networks for end to end speech recognition"

Connected through hyperedge HE-7 (induced by source paper "leveraging weakly supervised data to improve end to end speech to text translation"):
  with members:
    - "back translation style data augmentation for end to end asr"
    - "tied multitask learning for neural speech translation"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end automatic speech translation of audiobooks"
    - "multi modal data augmentation for end to end asr"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical generative modeling for controllable speech synthesis"

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)