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: "an end to end architecture for keyword spotting and voice activity detection"
  Abstract: We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurrent Neural Network trained with the Connectionist Temporal Classification loss function which allow our model to achieve high accuracy on both keyword spotting and voice activity detection without retraining. In contrast to prior voice activity detection models, our architecture does not require aligned training data and uses the same parameters as the keyword spotting model. This allows us to deploy a high quality voice activity detector with no additional memory or maintenance requirements.

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"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-2 (induced by source paper "orthogonality constrained multi head attention for keyword spotting"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "attention based end to end models for small footprint keyword spotting"
    - "multi head attention with disagreement regularization"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "trainable frontend for robust and far field keyword spotting"

Connected through hyperedge HE-3 (induced by source paper "streaming small footprint keyword spotting using sequence to sequence models"):
  with members:
    - "lstm a search space odyssey"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "end to end asr free keyword search from speech"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "personalized speech recognition on mobile devices"
    - "on the efficient representation and execution of deep acoustic models"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "convolutional recurrent neural networks for small footprint keyword spotting"):
  with members:
    - "trainable frontend for robust and far field keyword spotting"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "online keyword spotting with a character level recurrent neural network"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

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)