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: "raw waveform based audio classification using sample level cnn architectures"
  Abstract: Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics. However, as the image domain grows rapidly by versatile image classification models, it is necessary to study extensible classification models in the audio domain as well. In this study, we approach this problem using two types of sample-level deep convolutional neural networks that take raw waveforms as input and uses filters with small granularity. One is a basic model that consists of convolution and pooling layers. The other is an improved model that additionally has residual connections, squeeze-and-excitation modules and multi-level concatenation. We show that the sample-level models reach state-of-the-art performance levels for the three different categories of sound. Also, we visualize the filters along layers and compare the characteristics of learned filters.

Connected through hyperedge HE-1 (induced by source paper "fault diagnosis in microelectronics attachment via deep learning analysis of 3 d laser scans"):
  with members:
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pcanet a simple deep learning baseline for image classification"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-2 (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"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "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-3 (induced by source paper "spoken language identification using convnets"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "multiclass language identification using deep learning on spectral images of audio signals"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "mixup beyond empirical risk minimization"
    - "language identification using deep convolutional recurrent neural networks"
    - "cnn architectures for large scale audio classification"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "are nearby neighbors relatives diagnosing deep music embedding spaces"):
  with members:
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep learning and music adversaries"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)