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: "unsupervised feature learning based on deep models for environmental audio tagging"
  Abstract: Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper, we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a shrinking deep neural network DNN framework incorporating unsupervised feature learning to handle the multilabel classification task. For the acoustic modeling, a large set of contextual frames of the chunk are fed into the DNN to perform a multilabel classification for the expected tags, considering that only chunk or utterance level rather than frame-level labels are available. Dropout and background noise aware training are also adopted to improve the generalization capability of the DNNs. For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep denoising auto-encoder syDAE or asyDAE to generate new data-driven features from the logarithmic Mel-filter banks features. The new features, which are smoothed against background noise and more compact with contextual information, can further improve the performance of the DNN baseline. Compared with the standard Gaussian mixture model baseline of the DCASE 2016 audio tagging challenge, our proposed method obtains a significant equal error rate EER reduction from 0.21 to 0.13 on the development set. The proposed asyDAE system can get a relative 6.7% EER reduction compared with the strong DNN baseline on the development set. Finally, the results also show that our approach obtains the state-of-the-art performance with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while EER of the first prize of this challenge is 0.17.

Connected through hyperedge HE-1 (induced by source paper "limitations of weak labels for embedding and tagging"):
  with members:
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "massively parallel hyperparameter tuning"
    - "a closer look at weak label learning for audio events"
    - "training neural audio classifiers with few data"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "prototypical networks for few shot learning"
    - "learning from between class examples for deep sound recognition"
    - "learning from weak and noisy labels for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "audio set classification with attention model a probabilistic perspective"

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"
    - "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"
    - "mixup based acoustic scene classification using multi channel convolutional neural network"
    - "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 "enhancing sound texture in cnn based acoustic scene classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reducing model complexity for dnn based large scale audio classification"
    - "a joint detection classification model for audio tagging of weakly labelled data"
    - "cnn architectures for large scale audio classification"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "acoustic scene classification classifying environments from the sounds they produce"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (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"
    - "raw waveform based audio classification using sample level cnn architectures"
    - "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-6 (induced by source paper "training neural audio classifiers with few data"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "deep learning for audio transcription on low resource datasets"
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "unsupervised learning of semantic audio representations"
    - "language and noise transfer in speech enhancement generative adversarial network"
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"

Connected through hyperedge HE-7 (induced by source paper "deep learning for audio transcription on low resource datasets"):
  with members:
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "end to end learning for music audio tagging at scale"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"

Connected through hyperedge HE-8 (induced by source paper "sample mixed based data augmentation for domestic audio tagging"):
  with members:
    - "mixup based acoustic scene classification using multi channel convolutional neural network"
    - "shakedrop regularization for deep residual learning"
    - "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"

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)