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: "an unsupervised probability model for speech to translation alignment of low resource languages"
  Abstract: For many low-resource languages, spoken language resources are more likely to be annotated with translations than with transcriptions. Translated speech data is potentially valuable for documenting endangered languages or for training speech translation systems. A first step towards making use of such data would be to automatically align spoken words with their translations. We present a model that combines Dyer et al.'s reparameterization of IBM Model 2 (fast-align) and k-means clustering using Dynamic Time Warping as a distance metric. The two components are trained jointly using expectation-maximization. In an extremely low-resource scenario, our model performs significantly better than both a neural model and a strong baseline.

Connected through hyperedge HE-1 (induced by source paper "end to end speech translation with knowledge distillation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "large scale distributed neural network training through online distillation"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"

Connected through hyperedge HE-2 (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"
    - "structured based curriculum learning for end to end english japanese speech translation"
    - "sequence to sequence models can directly translate foreign speech"

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"
    - "unwritten languages demand attention too word discovery with encoder decoder models"
    - "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition"
    - "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 "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"
    - "a case study on using speech to translation alignments for language documentation"
    - "towards speech to text translation without speech recognition"
    - "effective approaches to attention based neural machine translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (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"
    - "unwritten languages demand attention too word discovery with encoder decoder models"

Connected through hyperedge HE-6 (induced by source paper "sequence to sequence models can directly translate foreign speech"):
  with members:
    - "towards speech to text translation without speech recognition"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "chained predictions using convolutional neural networks"
    - "listen and translate a proof of concept for end to end speech to text translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-7 (induced by source paper "towards speech to text translation without speech recognition"):
  with members:
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "weakly supervised spoken term discovery using cross lingual side information"
    - "listen and translate a proof of concept for end to end speech to text translation"

Connected through hyperedge HE-8 (induced by source paper "a case study on using speech to translation alignments for language documentation"):
  with members:
    - "weakly supervised spoken term discovery using cross lingual side information"

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)