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 "audio set classification with attention model a probabilistic perspective"):
  Abstract: This paper investigate the classification of the Audio Set dataset. Audio Set is a large scale multi instance learning (MIL) dataset of sound clips. In MIL, a bag consists of several instances, and a bag is labelled positive if one or more instances in the audio clip is positive. Audio Set is a MIL dataset because an audio clip is labelled positive for a class if at least one frame contains the corresponding class. We tackle this MIL problem using an attention model and explain this attention model from a novel probabilistic perspective. We define a probability space on each bag. Each instance in a bag has a trainable probability measure for a class. Then the classification of a bag is the expectation of the classification of the instances in the bag with respect to the learned probability measure. Experimental results show that our proposed attention model modeled by fully connected deep neural network obtains mAP of 0.327 on Audio Set dataset, outperforming the Google's baseline of 0.314 and recurrent neural network of 0.325.

Includes members:
  Member "unsupervised feature learning based on deep models for environmental audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "large scale weakly supervised audio classification using gated convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "audio event detection using weakly labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "deep learning for audio signal processing")

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)