human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unsupervised discovery of structured acoustic tokens with applications to spoken term detection"):
  Abstract: In this paper, we compare two paradigms for unsupervised discovery of structured acoustic tokens directly from speech corpora without any human annotation. The Multigranular Paradigm seeks to capture all available information in the corpora with multiple sets of tokens for different model granularities. The Hierarchical Paradigm attempts to jointly learn several levels of signal representations in a hierarchical structure. The two paradigms are unified within a theoretical framework in this paper. Query-by-Example Spoken Term Detection (QbE-STD) experiments on the QUESST dataset of MediaEval 2015 verifies the competitiveness of the acoustic tokens. The Enhanced Relevance Score (ERS) proposed in this work improves both paradigms for the task of QbE-STD. We also list results on the ABX evaluation task of the Zero Resource Challenge 2015 for comparison of the Paradigms.

Includes members:
  Member "a segmental framework for fully unsupervised large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "personalized acoustic modeling by weakly supervised multi task deep learning using acoustic tokens discovered from unlabeled data":
    also connected through other hyperedges:
      (none)
  Member "an iterative deep learning framework for unsupervised discovery of speech features and linguistic units with applications on spoken term detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion")
  Member "unsupervised spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection")
  Member "enhancing automatically discovered multi level acoustic patterns considering context consistency with applications in spoken term detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised iterative deep learning of speech features and acoustic tokens with applications to spoken term detection")
  Member "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries")

Question: Please tell me which class the source paper 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)