human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sample mixed based data augmentation for domestic audio tagging"):
  Abstract: Audio tagging has attracted increasing attention since last decade#R##N#and has various potential applications in many fields. The objective#R##N#of audio tagging is to predict the labels of an audio clip. Recently#R##N#deep learning methods have been applied to audio tagging and#R##N#have achieved state-of-the-art performance, which provides a poor#R##N#generalization ability on new data. However due to the limited size#R##N#of audio tagging data such as DCASE data, the trained models tend#R##N#to result in overfitting of the network. Previous data augmentation#R##N#methods such as pitch shifting, time stretching and adding background#R##N#noise do not show much improvement in audio tagging. In#R##N#this paper, we explore the sample mixed data augmentation for the#R##N#domestic audio tagging task, including mixup, SamplePairing and#R##N#extrapolation. We apply a convolutional recurrent neural network#R##N#(CRNN) with attention module with log-scaled mel spectrum as a#R##N#baseline system. In our experiments, we achieve an state-of-the-art#R##N#of equal error rate (EER) of 0.10 on DCASE 2016 task4 dataset#R##N#with mixup approach, outperforming the baseline system without#R##N#data augmentation.

Includes members:
  Member "mixup based acoustic scene classification using multi channel convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "shakedrop regularization for deep residual learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "unsupervised feature learning based on deep models for environmental audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "mixup beyond empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "shake shake regularization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimized generic feature learning for few shot classification across domains")
  Member "convolutional gated recurrent neural network incorporating spatial features for audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recurrent neural networks an embedded computing perspective")

Question: Please tell me which class the source paper 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)