human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
  Abstract: In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3-D) space. The proposed network takes a sequence of consecutive spectrogram time frames as input and maps it to two outputs in parallel. As the first output, the sound event detection (SED) is performed as a multi-label classification task on each time frame producing temporal activity for all the sound event classes. As the second output, localization is performed by estimating the 3-D Cartesian coordinates of the direction-of-arrival (DOA) for each sound event class using multi-output regression. The proposed method is able to associate multiple DOAs with respective sound event labels and further track this association with respect to time. The proposed method uses separately the phase and magnitude component of the spectrogram calculated on each audio channel as the feature, thereby avoiding any method- and array-specific feature extraction. The method is evaluated on five Ambisonic and two circular array format datasets with different overlapping sound events in anechoic, reverberant, and real-life scenarios. The proposed method is compared with two SED, three DOA estimation, and one SELD baselines. The results show that the proposed method is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios. The proposed method achieved a consistently higher recall of the estimated number of DOAs across datasets in comparison to the best baseline. Additionally, this recall was observed to be significantly better than the best baseline method for a higher number of overlapping sound events.

Connected through hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions"):
  with members:
    - "language modelling for sound event detection with teacher forcing and scheduled sampling"
    - "network decoupling from regular to depthwise separable convolutions"
    - "listening for sirens locating and classifying acoustic alarms in city scenes"
    - "convolutional recurrent neural networks for bird audio detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield"):
  with members:
    - "indoor sound source localization with probabilistic neural network"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "sound source localization in a multipath environment using convolutional neural networks"
    - "semi supervised source localization on multiple manifolds with distributed microphones"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "a volumetric srp with refinement step for sound source localization"
    - "audio surveillance a systematic review"

Connected through hyperedge HE-3 (induced by source paper "polyphonic sound event detection and localization using a two stage strategy"):
  with members:
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "indoor sound source localization with probabilistic neural network"
    - "deep neural networks for multiple speaker detection and localization"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"
    - "sound source localization in a multipath environment using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "audio tagging with noisy labels and minimal supervision"
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "a hybrid parametric deep learning approach for sound event localization and detection"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "model agnostic approaches to handling noisy labels when training sound event classifiers"
    - "a simple fusion of deep and shallow learning for acoustic scene classification"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mixup beyond empirical risk minimization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "end to end binaural sound localisation from the raw waveform"):
  with members:
    - "indoor sound source localization with probabilistic neural network"
    - "end to end polyphonic sound event detection using convolutional recurrent neural networks with learned time frequency representation input"
    - "multi speaker localization using convolutional neural network trained with noise"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-7 (induced by source paper "localization detection and tracking of multiple moving sound sources with a convolutional recurrent neural network"):
  with members:
    - "deep neural networks for multiple speaker detection and localization"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "audio surveillance a systematic review"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-8 (induced by source paper "joining sound event detection and localization through spatial segregation"):
  with members:
    - "the nigens general sound events database"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "comparing time and frequency domain for audio event recognition using deep learning"

Question: Please tell me which class the center node 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)