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 spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity"
  Abstract: This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT corpus.

Connected through hyperedge HE-1 (induced by source paper "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"):
  with members:
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "learning latent representations for speech generation and transformation"
    - "proximal policy optimization algorithms"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "neural architecture search with reinforcement learning"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

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"
    - "an iterative deep learning framework for unsupervised discovery of speech features and linguistic units with applications on spoken term detection"
    - "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:
    - "an iterative deep learning framework for unsupervised discovery of speech features and linguistic units with applications on spoken term detection"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "personalized acoustic modeling by weakly supervised multi task deep learning using acoustic tokens discovered from unlabeled data"):
  with members:
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"
    - "data selective transfer learning for multi domain speech recognition"

Connected through hyperedge HE-5 (induced by source paper "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"):
  with members:
    - "lstm a search space odyssey"
    - "unsupervised learning of video representations using lstms"
    - "deep convolutional acoustic word embeddings using word pair side information"
    - "distributed representations of sentences and documents"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (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:
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a multi layered acoustic tokenizing deep neural network mat dnn for unsupervised discovery of linguistic units and generation of high quality features"):
  with members:
    - "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"

Connected through hyperedge HE-8 (induced by source paper "enhancing automatically discovered multi level acoustic patterns considering context consistency with applications in spoken term detection"):
  with members:
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"

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)