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 gated recurrent neural network incorporating spatial features for audio tagging"
  Abstract: Environmental audio tagging is a newly proposed task to predict the presence or absence of a specific audio event in a chunk. Deep neural network (DNN) based methods have been successfully adopted for predicting the audio tags in the domestic audio scene. In this paper, we propose to use a convolutional neural network (CNN) to extract robust features from mel-filter banks (MFBs), spectrograms or even raw waveforms for audio tagging. Gated recurrent unit (GRU) based recurrent neural networks (RNNs) are then cascaded to model the long-term temporal structure of the audio signal. To complement the input information, an auxiliary CNN is designed to learn on the spatial features of stereo recordings. We evaluate our proposed methods on Task 4 (audio tagging) of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016) challenge. Compared with our recent DNN-based method, the proposed structure can reduce the equal error rate (EER) from 0.13 to 0.11 on the development set. The spatial features can further reduce the EER to 0.10. The performance of the end-to-end learning on raw waveforms is also comparable. Finally, on the evaluation set, we get the state-of-the-art performance with 0.12 EER while the performance of the best existing system is 0.15 EER.

Connected through hyperedge HE-1 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "deep cnn framework for audio event recognition using weakly labeled web data"

Connected through hyperedge HE-3 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization"):
  with members:
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"
    - "sound event detection using spatial features and convolutional recurrent neural network"

Connected through hyperedge HE-4 (induced by source paper "polyphonic sound event detection and localization using a two stage strategy"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "indoor sound source localization with probabilistic neural network"
    - "deep neural networks for multiple speaker detection and localization"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"

Connected through hyperedge HE-5 (induced by source paper "deep autotuner a data driven approach to natural sounding pitch correction for singing voice in karaoke performances"):
  with members:
    - "deep learning for singing processing achievements challenges and impact on singers and listeners"
    - "learning latent representations for speech generation and transformation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-7 (induced by source paper "deep learning for audio transcription on low resource datasets"):
  with members:
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "end to end learning for music audio tagging at scale"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"

Connected through hyperedge HE-8 (induced by source paper "sample mixed based data augmentation for domestic audio tagging"):
  with members:
    - "mixup based acoustic scene classification using multi channel convolutional neural network"
    - "shakedrop regularization for deep residual learning"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "mixup beyond empirical risk minimization"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "shake shake regularization"
    - "automatic tagging using deep convolutional neural networks"

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)