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: "a statistical model for word discovery in transcribed speech"
  Abstract: A statistical model for segmentation and word discovery in continuous speech is presented. An incremental unsupervised learning algorithm to infer word boundaries based on this model is described. Results of empirical tests showing that the algorithm is competitive with other models that have been used for similar tasks are also presented.

Connected through hyperedge HE-1 (induced by source paper "learning to discover ground and use words with segmental neural language models"):
  with members:
    - "revisiting the hierarchical multiscale lstm"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "unsupervised neural word segmentation for chinese via segmental language modeling"
    - "on the state of the art of evaluation in neural language models"
    - "representations of language in a model of visually grounded speech signal"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"

Connected through hyperedge HE-2 (induced by source paper "unsupervised word discovery with segmental neural language models"):
  with members:
    - "spell once summon anywhere a two level open vocabulary language model"
    - "unsupervised neural word segmentation for chinese via segmental language modeling"
    - "on the state of the art of evaluation in neural language models"
    - "representations of language in a model of visually grounded speech signal"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "unsupervised neural and bayesian models for zero resource speech processing"):
  with members:
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "symbol emergence in robotics a survey"
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"
    - "deep multimodal semantic embeddings for speech and images"
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "feature learning in deep neural networks studies on speech recognition tasks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"):
  with members:
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "an efficient probabilistically sound algorithm for segmentation and word discovery"

Connected through hyperedge HE-5 (induced by source paper "symbol emergence in robotics a survey"):
  with members:
    - "nonparametric bayesian double articulation analyzer for direct language acquisition from continuous speech signals"
    - "multimodal hierarchical dirichlet process based active perception"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"
    - "building high level features using large scale unsupervised learning"
    - "an efficient probabilistically sound algorithm for segmentation and word discovery"

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)