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 case study on using speech to translation alignments for language documentation"
  Abstract: For many low-resource or endangered languages, spoken language resources are more likely to be annotated with translations than with transcriptions. Recent work exploits such annotations to produce speech-to-translation alignments, without access to any text transcriptions. We investigate whether providing such information can aid in producing better (mismatched) crowdsourced transcriptions, which in turn could be valuable for training speech recognition systems, and show that they can indeed be beneficial through a small-scale case study as a proof-of-concept. We also present a simple phonetically aware string averaging technique that produces transcriptions of higher quality.

Connected through hyperedge HE-1 (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"
    - "attention is all you need"

Connected through hyperedge HE-2 (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 segmental framework for fully unsupervised large vocabulary speech recognition"
    - "a very low resource language speech corpus for computational language documentation experiments"
    - "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-3 (induced by source paper "low resource speech to text translation"):
  with members:
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "sequence to sequence models can directly translate foreign speech"
    - "towards speech to text translation without speech recognition"
    - "an unsupervised probability model for speech to translation alignment of low resource languages"
    - "effective approaches to attention based neural machine translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"):
  with members:
    - "sequence to sequence models can directly translate foreign speech"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "end to end automatic speech translation of audiobooks"):
  with members:
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "unwritten languages demand attention too word discovery with encoder decoder models"):
  with members:
    - "sequence to sequence models can directly translate foreign speech"
    - "six challenges for neural machine translation"
    - "weakly supervised spoken term discovery using cross lingual side information"
    - "fully character level neural machine translation without explicit segmentation"
    - "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:
    - "unwritten languages demand attention too word discovery with encoder decoder models"

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)