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 "learning discriminative and robust time frequency representations for environmental sound classification"):
  Abstract: Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, attention mechanisms have been used in CNN to capture the useful information from the audio signal for sound classification, especially for weakly labelled data where the timing information about the acoustic events is not available in the training data, apart from the availability of sound class labels. In these methods, however, the inherent time-frequency characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a new method, called time-frequency enhancement block (TFBlock), which temporal attention and frequency attention are employed to enhance the features from relevant frames and frequency bands. Compared with other attention mechanisms, in our method, parallel branches are constructed which allow the temporal and frequency features to be attended respectively in order to mitigate interference from the sections where no sound events happened in the acoustic environments. The experiments on three benchmark ESC datasets show that our method improves the classification performance and also exhibits robustness to noise.

Includes members:
  Member "multi stream network with temporal attention for environmental sound classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "audio visual model distillation using acoustic images")
  Member "attention based convolutional recurrent neural network for environmental sound classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "urban sound tagging using convolutional neural networks")
  Member "weakly labelled audioset tagging with attention neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection")
  Member "spatio temporal attention pooling for audio scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework")
  Member "learning environmental sounds with multi scale convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep progressive multi scale attention for acoustic event classification")
  Member "raw waveform based audio classification using sample level cnn architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fault diagnosis in microelectronics attachment via deep learning analysis of 3 d laser scans")
  Member "learning from between class examples for deep sound recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding and enhancing mixed sample data augmentation")
  Member "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")

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)