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: "nonparametric bayesian double articulation analyzer for direct language acquisition from continuous speech signals"
  Abstract: Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. Current machine learning methods cannot efficiently estimate language model (LM) and acoustic model (AM) and discover words directly from continuous human speech signals in an unsupervised manner. To solve this problem, we propose an integrative generative model that combines an LM and an AM into a single generative model called the hierarchical Dirichlet process hidden LM (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of LM and AM from continuous speech signals. Based on the HDP-HLM and its inference procedure, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire LM and AM from observed continuous speech signals. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer and baseline automatic speech recognition system whose AM was trained in a supervised manner. The main contributions of this paper are as follows: 1) we develop a probabilistic generative model that integrates LM and AM, i.e., HDP-HLM; 2) we derive an inference method for this, and propose the NPB-DAA; and 3) we show that the NPB-DAA can discover words directly from continuous human speech signals in an unsupervised manner.

Connected through hyperedge HE-1 (induced by source paper "neuro serket development of integrative cognitive system through the composition of deep probabilistic generative models"):
  with members:
    - "cross modal variational auto encoder with distributed latent spaces and associators"
    - "multimodal generative models for scalable weakly supervised learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "serket an architecture for connecting stochastic models to realize a large scale cognitive model"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "variational methods for conditional multimodal deep learning"

Connected through hyperedge HE-2 (induced by source paper "symbol emergence in cognitive developmental systems a survey"):
  with members:
    - "interactive natural language acquisition in a multi modal recurrent neural architecture"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "serket an architecture for connecting stochastic models to realize a large scale cognitive model"
    - "detection of social signals for recognizing engagement in human robot interaction"
    - "grounded language learning in a simulated 3d world"
    - "grounding spatio semantic referring expressions for human robot interaction"
    - "discovering objects and their relations from entangled scene representations"

Connected through hyperedge HE-3 (induced by source paper "an embedded segmental k means model for unsupervised segmentation and clustering of speech"):
  with members:
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "end to end asr free keyword search from speech"
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "multi view recurrent neural acoustic word embeddings"
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "revisiting k means new algorithms via bayesian nonparametrics"

Connected through hyperedge HE-4 (induced by source paper "symbol emergence in robotics a survey"):
  with members:
    - "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"
    - "a statistical model for word discovery in transcribed speech"
    - "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.AI(Artificial Intelligence)