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: "language transfer of audio word2vec learning audio segment representations without target language data"
  Abstract: Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence-to-sequence Autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as query-by-example Spoken Term Detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch phonetic structure from the audio segments of the target language if the source and target languages are similar. In query-by-example STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.

Connected through hyperedge HE-1 (induced by source paper "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "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"
    - "word translation without parallel data"

Connected through hyperedge HE-2 (induced by source paper "towards unsupervised automatic speech recognition trained by unaligned speech and text only"):
  with members:
    - "non adversarial unsupervised word translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "improved training of wasserstein gans"
    - "word translation without parallel data"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "adam a method for stochastic optimization"

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)