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 "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.

Includes members:
  Member "audio event detection using weakly labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "youtube 8m a large scale video classification benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "automatic tagging using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for audio signal processing")
  Member "yfcc100m the new data in multimedia research":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "in defense of grid features for visual question answering")
  Member "codebook based audio feature representation for music information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network")
  Member "automatic large scale classification of bird sounds is strongly improved by unsupervised feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nestedvae isolating common factors via weak supervision")

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)