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: "automatic grammar augmentation for robust voice command recognition"
  Abstract: This paper proposes a novel pipeline for automatic grammar augmentation that provides a significant improvement in the voice command recognition accuracy for systems with small footprint acoustic model (AM). The improvement is achieved by augmenting the user-defined voice command set, also called grammar set, with alternate grammar expressions. For a given grammar set, a set of potential grammar expressions (candidate set) for augmentation is constructed from an AM-specific statistical pronunciation dictionary that captures the consistent patterns and errors in the decoding of AM induced by variations in pronunciation, pitch, tempo, accent, ambiguous spellings, and noise conditions. Using this candidate set, greedy optimization based and cross-entropy-method (CEM) based algorithms are considered to search for an augmented grammar set with improved recognition accuracy utilizing a command-specific dataset. Our experiments show that the proposed pipeline along with algorithms considered in this paper significantly reduce the mis-detection and mis-classification rate without increasing the false-alarm rate. Experiments also demonstrate the consistent superior performance of CEM method over greedy-based algorithms.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"
    - "sequence discriminative training for deep learning based acoustic keyword spotting"

Connected through hyperedge HE-2 (induced by source paper "predicting detection filters for small footprint open vocabulary keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"
    - "deep residual learning for small footprint keyword spotting"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "hello edge keyword spotting on microcontrollers"

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)