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 "polyphonic sound event detection and localization using a two stage strategy"):
  Abstract: Sound event detection (SED) and localization refer to recognizing#R##N#sound events and estimating their spatial and temporal locations.#R##N#Using neural networks has become the prevailing method for SED.#R##N#In the area of sound localization, which is usually performed by estimating#R##N#the direction of arrival (DOA), learning-based methods have#R##N#recently been developed. In this paper, it is experimentally shown#R##N#that the trained SED model is able to contribute to the direction#R##N#of arrival estimation (DOAE). However, joint training of SED and#R##N#DOAE degrades the performance of both. Based on these results, a#R##N#two-stage polyphonic sound event detection and localization method#R##N#is proposed. The method learns SED first, after which the learned#R##N#feature layers are transferred for DOAE. It then uses the SED ground#R##N#truth as a mask to train DOAE. The proposed method is evaluated on#R##N#the DCASE 2019 Task 3 dataset, which contains different overlapping#R##N#sound events in different environments. Experimental results#R##N#show that the proposed method is able to improve the performance#R##N#of both SED and DOAE, and also performs significantly better than#R##N#the baseline method.

Includes members:
  Member "sound event localization and detection of overlapping sources using convolutional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems":
    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 "general purpose tagging of freesound audio with audioset labels task description dataset and baseline":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi representation knowledge distillation for audio classification")
  Member "indoor sound source localization with probabilistic neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield")
  Member "deep neural networks for multiple speaker detection and localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sound event localization and detection of overlapping sources using convolutional recurrent neural networks")
  Member "convolutional recurrent neural networks for polyphonic sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield")
  Member "broadband doa estimation using convolutional neural networks trained with noise signals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sound event localization and detection of overlapping sources using convolutional recurrent neural networks")

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)