human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (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"):
  Abstract: Producing a large amount of annotated speech data for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced. However, we note human babies start to learn the language by the sounds of a small number of exemplar words without hearing a large amount of data. We initiate some preliminary work in this direction in this paper. Audio Word2Vec is used to obtain embeddings of spoken words which carry phonetic information extracted from the signals. An autoencoder is used to generate embeddings of text words based on the articulatory features for the phoneme sequences. Both sets of embeddings for spoken and text words describe similar phonetic structures among words in their respective latent spaces. A mapping relation from the audio embeddings to text embeddings actually gives the word-level ASR. This can be learned by aligning a small number of spoken words and the corresponding text words in the embedding spaces. In the initial experiments only 200 annotated spoken words and one hour of speech data without annotation gave a word accuracy of 27.5%, which is low but a good starting point.

Includes members:
  Member "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "completely unsupervised phoneme recognition by a generative adversarial network harmonized with iteratively refined hidden markov models")
  Member "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "unsupervised cross modal alignment of speech and text embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "representation learning for electronic health records")
  Member "phonetic and semantic embedding of spoken words with applications in spoken content retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "non adversarial unsupervised word translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "towards unsupervised automatic speech recognition trained by unaligned speech and text only":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval")
  Member "query by example search with discriminative neural acoustic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech")
  Member "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving automated segmentation of radio shows with audio embeddings")

Question: Please tell me which class the source paper 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)