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: "enhancing automatically discovered multi level acoustic patterns considering context consistency with applications in spoken term detection"
  Abstract: This paper presents a novel approach for enhancing the multiple sets of acoustic patterns automatically discovered from a given corpus. In a previous work it was proposed that different HMM configurations (number of states per model, number of distinct models) for the acoustic patterns form a two-dimensional space. Multiple sets of acoustic patterns automatically discovered with the HMM configurations properly located on different points over this two-dimensional space were shown to be complementary to one another, jointly capturing the characteristics of the given corpus. By representing the given corpus as sequences of acoustic patterns on different HMM sets, the pattern indices in these sequences can be relabeled considering the context consistency across the different sequences. Good improvements were observed in preliminary experiments of pattern spoken term detection (STD) performed on both TIMIT and Mandarin Broadcast News with such enhanced patterns.

Connected through hyperedge HE-1 (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"
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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 "a multi layered acoustic tokenizing deep neural network mat dnn for unsupervised discovery of linguistic units and generation of high quality features"):
  with members:
    - "unsupervised spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity"
    - "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)