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: "a simple fusion of deep and shallow learning for acoustic scene classification"
  Abstract: In the past, Acoustic Scene Classification systems have been based on hand crafting audio features that are input to a classifier. Nowadays, the common trend is to adopt data driven techniques, e.g., deep learning, where audio representations are learned from data. In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machine. We first show that both methods provide complementary information to some extent. Then, we use a simple late fusion strategy to combine both methods. We report classification accuracy of each method individually and the combined system on the TUT Acoustic Scenes 2017 dataset. The proposed fused system outperforms each of the individual methods and attains a classification accuracy of 72.8% on the evaluation set, improving the baseline system by 11.8%.

Connected through hyperedge HE-1 (induced by source paper "model agnostic approaches to handling noisy labels when training sound event classifiers"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "audio tagging with noisy labels and minimal supervision"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "learning sound events from webly labeled data"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "learning sound event classifiers from web audio with noisy labels"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a closer look at weak label learning for audio events"
    - "joint optimization framework for learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-3 (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"
    - "sample dropout for audio scene classification using multi scale dense connected convolutional neural network"
    - "mixup based acoustic scene classification using multi channel 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-4 (induced by source paper "a hybrid parametric deep learning approach for sound event localization and detection"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "model agnostic approaches to handling noisy labels when training sound event classifiers"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mixup beyond empirical risk minimization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)