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: "an iterative deep learning framework for unsupervised discovery of speech features and linguistic units with applications on spoken term detection"
  Abstract: In this work we aim to discover high quality speech features and linguistic units directly from unlabeled speech data in a zero resource scenario. The results are evaluated using the metrics and corpora proposed in the Zero Resource Speech Challenge organized at Interspeech 2015. A Multi-layered Acoustic Tokenizer (MAT) was proposed for automatic discovery of multiple sets of acoustic tokens from the given corpus. Each acoustic token set is specified by a set of hyperparameters that describe the model configuration. These sets of acoustic tokens carry different characteristics fof the given corpus and the language behind, thus can be mutually reinforced. The multiple sets of token labels are then used as the targets of a Multi-target Deep Neural Network (MDNN) trained on low-level acoustic features. Bottleneck features extracted from the MDNN are then used as the feedback input to the MAT and the MDNN itself in the next iteration. We call this iterative deep learning framework the Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN), which generates both high quality speech features for the Track 1 of the Challenge and acoustic tokens for the Track 2 of the Challenge. In addition, we performed extra experiments on the same corpora on the application of query-by-example spoken term detection. The experimental results showed the iterative deep learning framework of MAT-DNN improved the detection performance due to better underlying speech features and acoustic tokens.

Connected through hyperedge HE-1 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "decoupled learning for conditional adversarial networks"
    - "improved audio embeddings by adjacency based clustering with applications in spoken term detection"
    - "voice impersonation using generative adversarial networks"
    - "unsupervised learning of semantic audio representations"
    - "tacotron towards end to end speech synthesis"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (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"
    - "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"
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"

Connected through hyperedge HE-3 (induced by source paper "unsupervised iterative deep learning of speech features and acoustic tokens with applications to 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"
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"
    - "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)