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: "mixup based acoustic scene classification using multi channel convolutional neural network"
  Abstract: Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and falls short of accuracy and efficiency. Recently, Convolutional Neural Network (CNN)-based methods have achieved better performance with comparison to the traditional methods. Nevertheless, conventional single channel CNN may fail to consider the fact that additional cues may be embedded in the multi-channel recordings. In this paper, we explore the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner. We conduct the evaluation compared with the conventional CNN and traditional Gaussian Mixture Model-based methods. Moreover, to improve the classification accuracy further, this paper explores the using of mixup method. In brief, mixup trains the neural network on linear combinations of pairs of the representation of audio scene examples and their labels. By employing the mixup approach for data argumentation, the novel model can provide higher prediction accuracy and robustness in contrast with previous models, while the generalization error can also be reduced on the evaluation data.

Connected through hyperedge HE-1 (induced by source paper "mixup regularization for region proposal based object detectors"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "towards adversarially robust object detection"
    - "bag of freebies for training object detection neural networks"
    - "learning to generate synthetic data via compositing"
    - "on physical adversarial patches for object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "regularization for deep learning a taxonomy"

Connected through hyperedge HE-2 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework"):
  with members:
    - "enhancing sound texture in cnn based acoustic scene classification"
    - "acoustic scene classification by implicitly identifying distinct sound events"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "spatio temporal attention pooling for audio scene classification"
    - "enabling early audio event detection with neural networks"
    - "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "beyond equal length snippets how long is sufficient to recognize an audio scene"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal attention pooling for audio scene classification"):
  with members:
    - "fine grained video categorization with redundancy reduction attention"
    - "beyond equal length snippets how long is sufficient to recognize an audio scene"
    - "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "learning from between class examples for deep sound recognition"
    - "audio scene classification with deep recurrent neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "label tree embeddings for acoustic scene classification"

Connected through hyperedge HE-4 (induced by source paper "general audio tagging with ensembling convolutional neural networks and statistical features"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a simple fusion of deep and shallow learning for acoustic scene classification"
    - "sample dropout for audio scene classification using multi scale dense connected convolutional neural network"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "sample mixed based data augmentation for domestic audio tagging"):
  with members:
    - "shakedrop regularization for deep residual learning"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "mixup beyond empirical risk minimization"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "shake shake regularization"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "sample dropout for audio scene classification using multi scale dense connected convolutional neural network"):
  with members:
    - "multi scale densenet based electricity theft detection"
    - "multi scale dense networks for resource efficient image classification"
    - "densely connected convolutional networks"
    - "soundnet learning sound representations from unlabeled video"
    - "deep residual learning for image recognition"
    - "anticipating visual representations from unlabeled video"
    - "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.CV(Computer Vision and Pattern Recognition)