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 "large scale weakly supervised audio classification using gated convolutional neural network"):
  Abstract: In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. The audio clips in this task, which are extracted from YouTube videos, are manually labeled with one or a few audio tags but without timestamps of the audio events, which is called as weakly labeled data. Two sub-tasks are defined in this challenge including audio tagging and sound event detection using this weakly labeled data. A convolutional recurrent neural network (CRNN) with learnable gated linear units (GLUs) non-linearity applied on the log Mel spectrogram is proposed. In addition, a temporal attention method is proposed along the frames to predicate the locations of each audio event in a chunk from the weakly labeled data. We ranked the 1st and the 2nd as a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6\% and Equal error 0.73, respectively.

Includes members:
  Member "convolutional recurrent neural networks for polyphonic sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "sound event detection using spatial features and convolutional recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization")
  Member "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "convolutional gated recurrent neural network incorporating spatial features for audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recurrent neural networks an embedded computing perspective")
  Member "language modeling with gated convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual lensing of universal sentence representations")
  Member "audio event detection using weakly labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "automatic tagging using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for audio signal processing")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)