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: "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"
  Abstract: Techniques for unsupervised discovery of acoustic patterns are getting increasingly attractive, because huge quantities of speech data are becoming available but manual annotations remain hard to acquire. In this paper, we propose an approach for unsupervised discovery of linguistic structure for the target spoken language given raw speech data. This linguistic structure includes two-level (subword-like and word-like) acoustic patterns, the lexicon of word-like patterns in terms of subword-like patterns and the N-gram language model based on word-like patterns. All patterns, models, and parameters can be automatically learned from the unlabelled speech corpus. This is achieved by an initialization step followed by three cascaded stages for acoustic, linguistic, and lexical iterative optimization. The lexicon of word-like patterns defines allowed consecutive sequence of HMMs for subword-like patterns. In each iteration, model training and decoding produces updated labels from which the lexicon and HMMs can be further updated. In this way, model parameters and decoded labels are respectively optimized in each iteration, and the knowledge about the linguistic structure is learned gradually layer after layer. The proposed approach was tested in preliminary experiments on a corpus of Mandarin broadcast news, including a task of spoken term detection with performance compared to a parallel test using models trained in a supervised way. Results show that the proposed system not only yields reasonable performance on its own, but is also complimentary to existing large vocabulary ASR systems.

Connected through hyperedge HE-1 (induced by source paper "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"):
  with members:
    - "sequence segmentation using joint rnn and structured prediction models"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "neural architectures for named entity recognition"
    - "improving phoneme segmentation with recurrent neural networks"
    - "end to end learning of action detection from frame glimpses in videos"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (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"
    - "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"
    - "a statistical model for word discovery in transcribed speech"

Connected through hyperedge HE-3 (induced by source paper "unsupervised discovery of structured acoustic tokens with applications to spoken term detection"):
  with members:
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "personalized acoustic modeling by weakly supervised multi task deep learning using acoustic tokens discovered from unlabeled data"
    - "an iterative deep learning framework for unsupervised discovery of speech features and linguistic units with applications on spoken term detection"
    - "unsupervised spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity"
    - "enhancing automatically discovered multi level acoustic patterns considering context consistency with applications in spoken term detection"

Connected through hyperedge HE-4 (induced by source paper "unsupervised iterative deep learning of speech features and acoustic tokens with applications to spoken term detection"):
  with members:
    - "an iterative deep learning framework for unsupervised discovery of speech features and linguistic units with applications on spoken term detection"
    - "unsupervised spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity"
    - "enhancing automatically discovered multi level acoustic patterns considering context consistency with applications in spoken term detection"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "a segmental framework for fully unsupervised large vocabulary speech recognition"):
  with members:
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "symbol emergence in robotics a survey"
    - "deep convolutional acoustic word embeddings using word pair side information"

Connected through hyperedge HE-6 (induced by source paper "personalized acoustic modeling by weakly supervised multi task deep learning using acoustic tokens discovered from unlabeled data"):
  with members:
    - "unsupervised spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity"
    - "data selective transfer learning for multi domain speech recognition"

Connected through hyperedge HE-7 (induced by source paper "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"):
  with members:
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "a statistical model for word discovery in transcribed speech"
    - "an efficient probabilistically sound algorithm for segmentation and word discovery"

Connected through hyperedge HE-8 (induced by source paper "an iterative deep learning framework for unsupervised discovery of speech features and linguistic units with applications on spoken term detection"):
  with members:
    - "unsupervised spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity"
    - "enhancing automatically discovered multi level acoustic patterns considering context consistency with applications in spoken term detection"
    - "caffe convolutional architecture for fast feature embedding"

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)