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: "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
  Abstract: While Word2Vec represents words (in text) as vectors carrying semantic information, audio Word2Vec was shown to be able to represent signal segments of spoken words as vectors carrying phonetic structure information. Audio Word2Vec can be trained in an unsupervised way from an unlabeled corpus, except the word boundaries are needed. In this paper, we extend audio Word2Vec from word-level to utterance-level by proposing a new segmental audio Word2Vec, in which unsupervised spoken word boundary segmentation and audio Word2Vec are jointly learned and mutually enhanced, so an utterance can be directly represented as a sequence of vectors carrying phonetic structure information. This is achieved by a segmental sequence-to-sequence autoencoder (SSAE), in which a segmentation gate trained with reinforcement learning is inserted in the encoder. Experiments on English, Czech, French and German show very good performance in both unsupervised spoken word segmentation and spoken term detection applications (significantly better than frame-based DTW).

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"
    - "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"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"

Connected through hyperedge HE-2 (induced by source paper "speechbert cross modal pre trained language model for end to end spoken question answering"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "semantic query by example speech search using visual grounding"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "low resource speech to text translation"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "visually grounded cross lingual keyword spotting in speech"
    - "speech2vec a sequence to sequence framework for learning word embeddings from 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"

Connected through hyperedge HE-4 (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"
    - "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"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"

Connected through hyperedge HE-5 (induced by source paper "towards unsupervised speech recognition and synthesis with quantized speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "an unsupervised autoregressive model for speech representation learning"
    - "vq wav2vec self supervised learning of discrete speech representations"
    - "wav2vec unsupervised pre training for speech recognition"
    - "representation learning with contrastive predictive coding"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "neural discrete representation learning"

Connected through hyperedge HE-6 (induced by source paper "distillation strategies for proximal policy optimization"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "low precision policy distillation with application to low power real time sensation cognition action loop with neuromorphic computing"
    - "model compression via distillation and quantization"
    - "learning transferable architectures for scalable image recognition"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-7 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "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"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"

Connected through hyperedge HE-8 (induced by source paper "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "state of the art speech recognition with sequence to sequence models"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "improved training of wasserstein gans"
    - "unsupervised iterative deep learning of speech features and acoustic tokens with applications to spoken term detection"
    - "towards speech to text translation without speech recognition"
    - "language transfer of audio word2vec learning audio segment representations without target language data"
    - "word translation without parallel data"

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)