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: "a very low resource language speech corpus for computational language documentation experiments"
  Abstract: Most speech and language technologies are trained with massive amounts of speech and text information. However, most of the world languages do not have such resources or stable orthography. Systems constructed under these almost zero resource conditions are not only promising for speech technology but also for computational language documentation. The goal of computational language documentation is to help field linguists to (semi-)automatically analyze and annotate audio recordings of endangered and unwritten languages. Example tasks are automatic phoneme discovery or lexicon discovery from the speech signal. This paper presents a speech corpus collected during a realistic language documentation process. It is made up of 5k speech utterances in Mboshi (Bantu C25) aligned to French text translations. Speech transcriptions are also made available: they correspond to a non-standard graphemic form close to the language phonology. We present how the data was collected, cleaned and processed and we illustrate its use through a zero-resource task: spoken term discovery. The dataset is made available to the community for reproducible computational language documentation experiments and their evaluation.

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"
    - "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"

Connected through hyperedge HE-2 (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-3 (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"
    - "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-4 (induced by source paper "exploring phoneme level speech representations for end to end speech translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "optimizing segmentation granularity for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "domain robust feature extraction for rapid low resource asr development"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"

Connected through hyperedge HE-5 (induced by source paper "empirical evaluation of sequence to sequence models for word discovery in low resource settings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adding interpretable attention to neural translation models improves word alignment"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "hybrid self attention network for machine translation"
    - "multi head attention with disagreement regularization"
    - "a case study on using speech to translation alignments for language documentation"

Connected through hyperedge HE-6 (induced by source paper "how does language influence documentation workflow unsupervised word discovery using translations in multiple languages"):
  with members:
    - "a small griko italian speech translation corpus"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "pre training on high resource speech recognition improves low resource speech to text translation"):
  with members:
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "multilingual bottleneck features for subword modeling in zero resource languages"
    - "a case study on using speech to translation alignments for language documentation"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised pretraining for sequence to sequence learning"
    - "transfer learning for low resource neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-8 (induced by source paper "tied multitask learning for neural speech translation"):
  with members:
    - "end to end automatic speech translation of audiobooks"
    - "towards speech to text translation without speech recognition"
    - "transfer learning across low resource related languages for neural machine translation"
    - "unwritten languages demand attention too word discovery with encoder decoder models"
    - "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition"
    - "an unsupervised probability model for speech to translation alignment of low resource languages"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "listen and translate a proof of concept for end to end 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)