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: "speech vocoding for laboratory phonology"
  Abstract: In this paper, we propose a platform based on phonological speech vocoding for examining relations between phonology and speech processing, and in broader terms, between the abstract and physical structures of speech signal. The goal of this paper is to go a step further towards bridging both fields and contributing to the program of Laboratory Phonology. We show two application examples for the laboratory phonology: a comparison of the phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The following three phonological systems are considered in this work: (i) Government Phonology (GP) features, (ii) the Sound Pattern of English (SPE) features, and (iii) the extended SPE (eSPE) features. Comparing GP- and eSPE-based vocoded speech, we con- clude that eSPE achieves slightly better results than the other two systems. However, GP – the most compact phonological speech representation – performs comparably to the systems with higher number of phonological classes. The parametric TTS based on phonological speech representation, and trained from an unlabelled audio book in an unsupervised manner, achieves promising results. We envision that the presented approach paves the way for researchers in both fields to form mean- ingful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. Laboratory phonologists might test the applied concepts of their theoretical models and speech pro- cessing community may be suggesting tests for the potential of advancing the performance of current state-of-the-art applications using the concepts developed for the theoretical phonological models.

Connected through hyperedge HE-1 (induced by source paper "composition of deep and spiking neural networks for very low bit rate speech coding"):
  with members:
    - "on structured sparsity of phonological posteriors for linguistic parsing"
    - "small footprint deep neural networks with highway connections for speech recognition"

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)