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: "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
  Abstract: Word embedding or Word2Vec has been successful in offering semantics for text words learned from the context of words. Audio Word2Vec was shown to offer phonetic structures for spoken words (signal segments for words) learned from signals within spoken words. This paper proposes a two-stage framework to perform phonetic-and-semantic embedding on spoken words considering the context of the spoken words. Stage 1 performs phonetic embedding with speaker characteristics disentangled. Stage 2 then performs semantic embedding in addition. We further propose to evaluate the phonetic-and-semantic nature of the audio embeddings obtained in Stage 2 by parallelizing with text embeddings. In general, phonetic structure and semantics inevitably disturb each other. For example the words "brother" and "sister" are close in semantics but very different in phonetic structure, while the words "brother" and "bother" are in the other way around. But phonetic-and-semantic embedding is attractive, as shown in the initial experiments on spoken document retrieval. Not only spoken documents including the spoken query can be retrieved based on the phonetic structures, but spoken documents semantically related to the query but not including the query can also be retrieved based on the semantics.

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"
    - "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 "completely unsupervised phoneme recognition by a generative adversarial network harmonized with iteratively refined hidden markov models"):
  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"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised machine translation using monolingual corpora only"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "unsupervised sequence classification using sequential output statistics"
    - "unsupervised neural machine translation"

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"
    - "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"
    - "low resource speech to text translation"
    - "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 "acoustically grounded word embeddings for improved acoustics to word speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "end to end asr free keyword search from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-5 (induced by source paper "aipnet generative adversarial pre training of accent invariant networks for end to end speech recognition"):
  with members:
    - "domain adversarial training for accented speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "invariant representations for noisy speech recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (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"
    - "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"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "improved audio embeddings by adjacency based clustering with applications in spoken term detection"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "unsupervised learning of semantic audio representations"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "deep convolutional acoustic word embeddings using word pair side information"

Connected through hyperedge HE-8 (induced by source paper "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"):
  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"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"

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)