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: "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
  Abstract: In settings where only unlabeled speech data is available, speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language modelling text. A similar problem is faced when modeling infant language acquisition. In these cases, categorical linguistic structure needs to be discovered directly from speech audio. We present a novel unsu-pervised Bayesian model that segments unlabeled speech and clusters the segments into hypothesized word groupings. The result is a complete unsupervised tokenization of the input speech in terms of discovered word types. In our approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional acoustic vector space. The model, implemented as a Gibbs sampler, then builds a whole-word acoustic model in this space while jointly performing segmentation. We report word error rates in a small-vocabulary connected digit recognition task by mapping the unsupervised decoded output to ground truth transcriptions. The model achieves around 20% error rate, outperforming a previous HMM-based system by about 10% absolute. Moreover, in contrast to the baseline, our model does not require a pre-specified vocabulary size.

Connected through hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "language learning using speech to image retrieval"
    - "towards visually grounded sub word speech unit discovery"
    - "the zero resource speech challenge 2019 tts without t"
    - "word recognition competition and activation in a model of visually grounded speech"

Connected through hyperedge HE-2 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "visual features for context aware speech recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "learning word like units from joint audio visual analysis"
    - "learning modality invariant representations for speech and images"

Connected through hyperedge HE-3 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "assessing composition in sentence vector representations"
    - "an analysis of neural language modeling at multiple scales"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-4 (induced by source paper "large scale representation learning from visually grounded untranscribed speech"):
  with members:
    - "towards visually grounded sub word speech unit discovery"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "benchmark analysis of representative deep neural network architectures"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "efficient natural language response suggestion for smart reply"

Connected through hyperedge HE-5 (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-6 (induced by source paper "jointly discovering visual objects and spoken words from raw sensory input"):
  with members:
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "learning word like units from joint audio visual analysis"
    - "unsupervised learning of semantic audio representations"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "cnn features are also great at unsupervised classification"
    - "look listen and learn"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-7 (induced by source paper "unsupervised cross modal alignment of speech and text embedding spaces"):
  with members:
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised machine translation using monolingual corpora only"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"

Connected through hyperedge HE-8 (induced by source paper "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"):
  with members:
    - "learning word like units from joint audio visual analysis"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "representations of language in a model of visually grounded speech signal"
    - "generative adversarial text to image synthesis"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "achieving human parity in conversational speech recognition"
    - "vqa visual question answering"

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)