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: "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"
  Abstract: The CMU Wilderness Multilingual Speech Dataset (Black, 2019) is a newly published multilingual speech dataset based on recorded readings of the New Testament. It provides data to build Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models for potentially 700 languages. However, the fact that the source content (the Bible) is the same for all the languages is not exploited to date.Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). We name this corpus MaSS (Multilingual corpus of Sentence-aligned Spoken utterances). The covered languages (Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish) allow researches on speech-to-speech alignment as well as on translation for typologically different language pairs. The quality of the final corpus is attested by human evaluation performed on a corpus subset (100 utterances, 8 language pairs). Lastly, we showcase the usefulness of the final product on a bilingual speech retrieval task.

Connected through hyperedge HE-1 (induced by source paper "covost a diverse multilingual speech to text translation corpus"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual end to end speech translation"
    - "harnessing indirect training data for end to end automatic speech translation tricks of the trade"
    - "europarl st a multilingual corpus for speech translation of parliamentary debates"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "librivoxdeen a corpus for german to english speech translation and speech recognition"
    - "vizseq a visual analysis toolkit for text generation tasks"

Connected through hyperedge HE-2 (induced by source paper "breaking the data barrier towards robust speech translation via adversarial stability training"):
  with members:
    - "towards robust neural machine translation"
    - "towards end to end spoken language understanding"
    - "end to end automatic speech translation of audiobooks"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "improving the robustness of speech translation"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "europarl st a multilingual corpus for speech translation of parliamentary debates"):
  with members:
    - "exploring phoneme level speech representations for end to end speech translation"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "direct speech to speech translation with a sequence to sequence model"
    - "a call for clarity in reporting bleu scores"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "neural machine translation of rare words with subword units"

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)