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: "unwritten languages demand attention too word discovery with encoder decoder models"
  Abstract: Word discovery is the task of extracting words from unsegmented text. In this paper we examine to what extent neural networks can be applied to this task in a realistic unwritten language scenario, where only small corpora and limited annotations are available. We investigate two scenarios: one with no supervision and another with limited supervision with access to the most frequent words. Obtained results show that it is possible to retrieve at least 27% of the gold standard vocabulary by training an encoder-decoder neural machine translation system with only 5,157 sentences. This result is close to those obtained with a task-specific Bayesian nonparametric model. Moreover, our approach has the advantage of generating translation alignments, which could be used to create a bilingual lexicon. As a future perspective, this approach is also well suited to work directly from speech.

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 "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "tied multitask learning for neural speech translation"):
  with members:
    - "end to end automatic speech translation of audiobooks"
    - "a very low resource language speech corpus for computational language documentation experiments"
    - "towards speech to text translation without speech recognition"
    - "transfer learning across low resource related languages for neural machine translation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a small griko italian speech translation corpus"):
  with members:
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "part of speech tagging on an endangered language a parallel griko italian resource"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "sequence to sequence models can directly translate foreign speech"
    - "a very low resource language speech corpus for computational language documentation experiments"
    - "towards speech to text translation without speech recognition"
    - "weakly supervised spoken term discovery using cross lingual side information"

Connected through hyperedge HE-5 (induced by source paper "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"):
  with members:
    - "sequence to sequence models can directly translate foreign speech"
    - "speech coco 600k visually grounded spoken captions aligned to mscoco data set"
    - "a very low resource language speech corpus for computational language documentation experiments"
    - "representations of language in a model of visually grounded speech signal"
    - "listen and translate a proof of concept for end to end speech to text translation"
    - "listen attend and spell"
    - "deep multimodal semantic embeddings for speech and images"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-6 (induced by source paper "unsupervised word segmentation from speech with attention"):
  with members:
    - "bayesian models for unit discovery on a very low resource language"
    - "sequence to sequence models can directly translate foreign speech"
    - "listen and translate a proof of concept for end to end speech to text translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "a very low resource language speech corpus for computational language documentation experiments"):
  with members:
    - "a case study on using speech to translation alignments for language documentation"

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)