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 segmental framework for fully unsupervised large vocabulary speech recognition"
  Abstract: Abstract   Zero-resource speech technology is a growing research area that aims to develop methods for speech processing in the absence of transcriptions, lexicons, or language modelling text. Early term discovery systems focused on identifying isolated recurring patterns in a corpus, while more recent full-coverage systems attempt to completely segment and cluster the audio into word-like units—effectively performing unsupervised speech recognition. This article presents the first attempt we are aware of to apply such a system to large-vocabulary multi-speaker data. Our system uses a Bayesian modelling framework with segmental word representations: each word segment is represented as a fixed-dimensional acoustic embedding obtained by mapping the sequence of feature frames to a single embedding vector. We compare our system on English and Xitsonga datasets to state-of-the-art baselines, using a variety of measures including word error rate (obtained by mapping the unsupervised output to ground truth transcriptions). Very high word error rates are reported—in the order of 70–80% for speaker-dependent and 80–95% for speaker-independent systems—highlighting the difficulty of this task. Nevertheless, in terms of cluster quality and word segmentation metrics, we show that by imposing a consistent top-down segmentation while also using bottom-up knowledge from detected syllable boundaries, both single-speaker and multi-speaker versions of our system outperform a purely bottom-up single-speaker syllable-based approach. We also show that the discovered clusters can be made less speaker- and gender-specific by using an unsupervised autoencoder-like feature extractor to learn better frame-level features (prior to embedding). Our system’s discovered clusters are still less pure than those of unsupervised term discovery systems, but provide far greater coverage.

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 "unsupervised speech representation learning using wavenet autoencoders"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "disentangled sequential autoencoder"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-3 (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-4 (induced by source paper "wav2vec unsupervised pre training for speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pre trained language model representations for language generation"
    - "cloze driven pretraining of self attention networks"
    - "who needs words lexicon free speech recognition"
    - "data efficient image recognition with contrastive predictive coding"
    - "cross lingual language model pretraining"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "improving speech emotion recognition via transformer based predictive coding through transfer learning"

Connected through hyperedge HE-5 (induced by source paper "learning joint acoustic phonetic word embeddings"):
  with members:
    - "a survey of multi view representation learning"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "sampling matters in deep embedding learning"
    - "learning word like units from joint audio visual analysis"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "learning word embeddings from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-6 (induced by source paper "from semi supervised to almost unsupervised speech recognition with very low resource by jointly learning phonetic structures from audio and text embeddings"):
  with members:
    - "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "end to end neural segmental models for speech recognition"
    - "improved training of wasserstein gans"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"

Connected through hyperedge HE-7 (induced by source paper "towards visually grounded sub word speech unit discovery"):
  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"
    - "learning word like units from joint audio visual analysis"
    - "unsupervised learning of semantic audio representations"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "representations of language in a model of visually grounded speech signal"

Connected through hyperedge HE-8 (induced by source paper "a computational model of early language acquisition from audiovisual experiences of young infants"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "computational and robotic models of early language development a review"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "the zero resource speech challenge 2017"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "language bootstrapping learning word meanings from perception action association"

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)