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: "learning environmental sounds with multi scale convolutional neural network"
  Abstract: Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce two-phase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.

Connected through hyperedge HE-1 (induced by source paper "deep progressive multi scale attention for acoustic event classification"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "attention based deep multiple instance learning"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "attention based convolutional recurrent neural network for environmental sound classification"):
  with members:
    - "multi stream network with temporal attention for environmental sound classification"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "learning from between class examples for deep sound recognition"
    - "mixup beyond empirical risk minimization"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "soundnet learning sound representations from unlabeled video"
    - "acoustic scene classification classifying environments from the sounds they produce"

Connected through hyperedge HE-3 (induced by source paper "learning discriminative and robust time frequency representations for environmental sound classification"):
  with members:
    - "multi stream network with temporal attention for environmental sound classification"
    - "attention based convolutional recurrent neural network for environmental sound classification"
    - "weakly labelled audioset tagging with attention neural networks"
    - "spatio temporal attention pooling for audio scene classification"
    - "raw waveform based audio classification using sample level cnn architectures"
    - "learning from between class examples for deep sound recognition"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "multi stream network with temporal attention for environmental sound classification"):
  with members:
    - "a simple model for detection of rare sound events"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "learning from between class examples for deep sound recognition"
    - "temporal attention model for neural machine translation"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-5 (induced by source paper "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"):
  with members:
    - "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"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"

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)