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 embedded segmental k means model for unsupervised segmentation and clustering of speech"
  Abstract: Unsupervised segmentation and clustering of unlabelled speech are core problems in zero-resource speech processing. Most approaches lie at methodological extremes: some use probabilistic Bayesian models with convergence guarantees, while others opt for more efficient heuristic techniques. Despite competitive performance in previous work, the full Bayesian approach is difficult to scale to large speech corpora. We introduce an approximation to a recent Bayesian model that still has a clear objective function but improves efficiency by using hard clustering and segmentation rather than full Bayesian inference. Like its Bayesian counterpart, this embedded segmental K-means model (ES-KMeans) represents arbitrary-length word segments as fixed-dimensional acoustic word embeddings. We first compare ES-KMeans to previous approaches on common English and Xitsonga data sets (5 and 2.5 hours of speech): ES-KMeans outperforms a leading heuristic method in word segmentation, giving similar scores to the Bayesian model while being 5 times faster with fewer hyperparameters. However, its clusters are less pure than those of the other models. We then show that ES-KMeans scales to larger corpora by applying it to the 5 languages of the Zero Resource Speech Challenge 2017 (up to 45 hours), where it performs competitively compared to the challenge baseline.

Connected through hyperedge HE-1 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages"):
  with members:
    - "learned in speech recognition contextual acoustic word embeddings"
    - "audio linguistic embeddings for spoken sentences"
    - "bayesian subspace hidden markov model for acoustic unit discovery"
    - "massively multilingual adversarial speech recognition"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "truly unsupervised acoustic word embeddings using weak top down constraints in encoder decoder models"

Connected through hyperedge HE-2 (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-3 (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"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "improved training of wasserstein gans"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"

Connected through hyperedge HE-4 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "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 adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental models for speech recognition"

Connected through hyperedge HE-5 (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"
    - "word translation without parallel data"

Connected through hyperedge HE-6 (induced by source paper "towards unsupervised speech to text translation"):
  with members:
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "unsupervised statistical machine translation"
    - "end to end automatic speech translation of audiobooks"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-7 (induced by source paper "truly unsupervised acoustic word embeddings using weak top down constraints in encoder decoder models"):
  with members:
    - "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"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "end to end asr free keyword search from speech"
    - "the zero resource speech challenge 2017"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "sequence prediction with neural segmental models"):
  with members:
    - "optimizing expected word error rate via sampling for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "wav2letter an end to end convnet based speech recognition system"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "end to end training approaches for discriminative segmental models"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "advances in all neural speech recognition"

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)