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: "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"
  Abstract: In recent years, neural network approaches have shown superior performance to conventional hand-made features in numerous application areas. In particular, convolutional neural networks (ConvNets) exploit spatially local correlations across input data to improve the performance of audio processing tasks, such as speech recognition, musical chord recognition, and onset detection. Here we apply ConvNet to acoustic scene classification, and show that the error rate can be further decreased by using delta features in the frequency domain. We propose a multiple-width frequency-delta (MWFD) data augmentation method that uses static mel-spectrogram and frequency-delta features as individual input examples. In addition, we describe a ConvNet output aggregation method designed for MWFD augmentation, folded mean aggregation, which combines output probabilities of static and MWFD features from the same analysis window using multiplication first, rather than taking an average of all output probabilities. We describe calculation results using the DCASE 2016 challenge dataset, which shows that ConvNet outperforms both of the baseline system with hand-crafted features and a deep neural network approach by around 7%. The performance was further improved (by 5.7%) using the MWFD augmentation together with folded mean aggregation. The system exhibited a classification accuracy of 0.831 when classifying 15 acoustic scenes.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search on acoustic scene classification"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "stochastic adaptive neural architecture search for keyword spotting"
    - "darts differentiable architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "dcase 2019 cnn depth analysis with different channel inputs for acoustic scene classification"):
  with members:
    - "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "xception deep learning with depthwise separable convolutions"
    - "soundnet learning sound representations from unlabeled video"
    - "understanding audio pattern using convolutional neural network from raw waveforms"

Connected through hyperedge HE-3 (induced by source paper "neurostream scalable and energy efficient deep learning with smart memory cubes"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "a near threshold risc v core with dsp extensions for scalable iot endpoint devices"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "identity mappings in deep residual networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "fathom reference workloads for modern deep learning methods"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "recent advances in convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"):
  with members:
    - "learning environmental sounds with multi scale convolutional neural network"
    - "mixup based acoustic scene classification using multi channel convolutional neural network"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "very deep convolutional neural networks for raw waveforms"
    - "densely connected convolutional networks"
    - "hierarchical learning for dnn based acoustic scene classification"

Connected through hyperedge HE-5 (induced by source paper "mixup based acoustic scene classification using multi channel convolutional neural network"):
  with members:
    - "mixup beyond empirical risk minimization"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "label tree embeddings for acoustic scene classification"
    - "classifying variable length audio files with all convolutional networks and masked global pooling"
    - "xception deep learning with depthwise separable convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "a joint detection classification model for audio tagging of weakly labelled data"):
  with members:
    - "audio event detection using weakly labeled data"
    - "automatic tagging using deep convolutional neural networks"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

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)