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 "dcase 2019 cnn depth analysis with different channel inputs for acoustic scene classification"):
  Abstract: The objective of this technical report is to describe the framework used in Task 1, Acoustic scene classification (ASC), of the DCASE 2019 challenge. The presented approach is based on Log-Mel spectrogram representations and VGG-based Convolutional Neural Networks (CNNs). Three different CNNs, with very similar architectures, have been implemented. Themain difference is the number of filters in their convolutional blocks. Experiments show that the depth of the network is not the most relevant factor for improving the accuracy of the results.The performance seems to be more sensitive to the input audio representation. This conclusion is important for the implementation of real-time audio recognition and classification systemon edge devices. In the presented experiments the best audio representation is the Log-Mel spectrogram of the harmonic andpercussive sources plus the Log-Mel spectrogram of the difference between left and right stereo-channels. Also, in order to improve accuracy, ensemble methods combining different model predictions with different inputs are explored. Besides geometric and arithmetic means, ensembles aggregated with the Orness Weighted Averaged (OWA) operator have shown interesting andnovel results. The proposed framework outperforms the baseline system by 14.34 percentage points. For Task 1a, the obtained development accuracy is 76.84 percent, being 62.5 percent the baseline, whereas the accuracy obtained in public leaderboard is 77.33 percent,being 64.33 percent the baseline.

Includes members:
  Member "acoustic scene classification using multi layer temporal pooling based on convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the performance of residual block design alternatives in convolutional neural networks for end to end audio classification")
  Member "dcase 2018 challenge surrey cross task convolutional neural network baseline":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework")
  Member "dcase 2018 challenge baseline with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cure dataset ladder networks for audio event classification")
  Member "deep convolutional neural networks and data augmentation for environmental sound classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")
  Member "large scale weakly supervised audio classification using gated convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "xception deep learning with depthwise separable convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "soundnet learning sound representations from unlabeled video":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural architecture search on acoustic scene classification")

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)