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 "data efficient weakly supervised learning for low resource audio event detection using deep learning"):
  Abstract: We propose a method to perform audio event detection under the common constraint that only limited training data are available. In training a deep learning system to perform audio event detection, two practical problems arise. Firstly, most datasets are "weakly labelled" having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose a data-efficient training of a stacked convolutional and recurrent neural network. This neural network is trained in a multi instance learning setting for which we introduce a new loss function that leads to improved training compared to the usual approaches for weakly supervised learning. We successfully test our approach on two low-resource datasets that lack temporal labels.

Includes members:
  Member "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection")
  Member "unsupervised feature learning based on deep models for environmental audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "densely connected cnns for bird audio detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust sound event detection in bioacoustic sensor networks")
  Member "deep cnn framework for audio event recognition using weakly labeled web data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "stacked convolutional and recurrent neural networks for bird audio detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "deep convolutional neural networks and data augmentation for environmental sound classification":
    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 "convolutional gated recurrent neural network incorporating spatial features for audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recurrent neural networks an embedded computing perspective")

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)