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: "attention based end to end models for small footprint keyword spotting"
  Abstract: In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system. Our model consists of an encoder and an attention mechanism. The encoder transforms the input signal into a high level representation using RNNs. Then the attention mechanism weights the encoder features and generates a fixed-length vector. Finally, by linear transformation and softmax function, the vector becomes a score used for keyword detection. We also evaluate the performance of different encoder architectures, including LSTM, GRU and CRNN. Experiments on real-world wake-up data show that our approach outperforms the recent Deep KWS approach by a large margin and the best performance is achieved by CRNN. To be more specific, with ~84K parameters, our attention-based model achieves 1.02% false rejection rate (FRR) at 1.0 false alarm (FA) per hour.

Connected through hyperedge HE-1 (induced by source paper "robust sound event detection in bioacoustic sensor networks"):
  with members:
    - "learning to detect dysarthria from raw speech"
    - "recognizing birds from sound the 2018 birdclef baseline system"
    - "convolutional recurrent neural networks for bird audio detection"
    - "densely connected cnns for bird audio detection"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deformable convolutional networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "reducing bias in production speech models"

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"
    - "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"
    - "an end to end architecture for keyword spotting and voice activity detection"

Connected through hyperedge HE-3 (induced by source paper "query by example on device keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "meta learning for few shot keyword spotting"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "end to end asr free keyword search from speech"
    - "trainable frontend for robust and far field keyword spotting"

Connected through hyperedge HE-4 (induced by source paper "a monaural speech enhancement method for robust small footprint keyword spotting"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "deep residual learning for small footprint keyword spotting"
    - "honk a pytorch reimplementation of convolutional neural networks for keyword spotting"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "few shot learning with attention based sequence to sequence models"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "prototypical networks for few shot learning"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "learning to compare relation network for few shot learning"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "attention based models for speech recognition"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "meta learning for few shot keyword spotting"):
  with members:
    - "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"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "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-7 (induced by source paper "end to end models with auditory attention in multi channel keyword spotting"):
  with members:
    - "convolutional recurrent neural networks 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-8 (induced by source paper "sequence to sequence models for small footprint keyword spotting"):
  with members:
    - "state of the art speech recognition with sequence to sequence models"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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.SD(Sound)