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 "environment sound classification using multiple feature channels and attention based deep convolutional neural network"):
  Abstract: In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. And, we employ a deeper CNN (DCNN) compared to previous models, consisting of spatially separable convolutions working on time and feature domain separately. Alongside, we use attention modules that perform channel and spatial attention together. We use some data augmentation techniques to further boost performance. Our model is able to achieve state-of-the-art performance on all three benchmark environment sound classification datasets, i.e. the UrbanSound8K (97.52%), ESC-10 (95.75%) and ESC-50 (88.50%). To the best of our knowledge, this is the first time that a single environment sound classification model is able to achieve state-of-the-art results on all three datasets. For ESC-10 and ESC-50 datasets, the accuracy achieved by the proposed model is beyond human accuracy of 95.7% and 81.3% respectively.

Includes members:
  Member "end to end environmental sound classification using a 1d convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "universal adversarial audio perturbations")
  Member "multi stream network with temporal attention for environmental sound classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "audio visual model distillation using acoustic images")
  Member "randomly weighted cnns for music audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised learning by cross modal audio video clustering")
  Member "study of residual networks for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "densenet models for tiny imagenet classification")
  Member "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for audio signal processing")
  Member "deep convolutional neural network with mixup for environmental sound classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention based convolutional recurrent neural network for environmental sound classification")
  Member "comparison of time frequency representations for environmental sound classification using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")
  Member "learning from between class examples for deep sound recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding and enhancing mixed sample data augmentation")

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)