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: "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.

Connected through hyperedge HE-1 (induced by source paper "multi representation knowledge distillation for audio classification"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "paraphrasing complex network network compression via factor transfer"
    - "weakly supervised crnn system for sound event detection with large scale unlabeled in domain data"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "mixup beyond empirical risk minimization"
    - "cooperative learning with visual attributes"

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)