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: "convolutional recurrent neural networks for small footprint keyword spotting"
  Abstract: Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs). Inspired by large-scale state-of-the-art speech recognition systems, we combine the strengths of convolutional layers and recurrent layers to exploit local structure and long-range context. We analyze the effect of architecture parameters, and propose training strategies to improve performance. With only ~230k parameters, our CRNN model yields acceptably low latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise ratio.

Connected through hyperedge HE-1 (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"
    - "trainable frontend for robust and far field keyword spotting"
    - "an end to end architecture for keyword spotting and voice activity detection"

Connected through hyperedge HE-2 (induced by source paper "meta learning for few shot keyword spotting"):
  with members:
    - "attention based end to end models for small footprint keyword spotting"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "hello edge keyword spotting on microcontrollers"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "very deep convolutional networks for end to end speech recognition"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "javascript convolutional neural networks for keyword spotting in the browser an experimental analysis"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "adaptive pruning of neural language models for mobile devices"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "an experimental analysis of the power consumption of convolutional neural networks for keyword spotting"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "attention based end to end models for small footprint keyword spotting"):
  with members:
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "a neural attention model for abstractive sentence summarization"
    - "end to end attention based large vocabulary speech recognition"
    - "online keyword spotting with a character level recurrent neural network"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "streaming voice query recognition using causal convolutional recurrent neural networks"):
  with members:
    - "deep residual learning for small footprint keyword spotting"
    - "state of the art speech recognition with sequence to sequence models"
    - "learning intrinsic sparse structures within long short term memory"
    - "comparing human and machine errors in conversational speech transcription"
    - "listening to the world improves speech command recognition"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "the microsoft 2016 conversational speech recognition system"

Connected through hyperedge HE-6 (induced by source paper "end to end models with auditory attention in multi channel keyword spotting"):
  with members:
    - "attention based end to end models for small footprint keyword spotting"
    - "recurrent models for auditory attention in multi microphone distance speech recognition"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "on adjusting the learning rate in frequency domain echo cancellation with double talk"

Connected through hyperedge HE-7 (induced by source paper "sequence to sequence models for small footprint keyword spotting"):
  with members:
    - "attention based end to end models for small footprint keyword spotting"
    - "state of the art speech recognition with sequence to sequence models"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "hello edge keyword spotting on microcontrollers"):
  with members:
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "the microsoft 2017 conversational speech recognition system"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dual path networks"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)