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: "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
  Abstract: This paper proposes a deep neural network for estimating the directions of arrival (DOA) of multiple sound sources. The proposed stacked convolutional and recurrent neural network (DOAnet) generates a spatial pseudo-spectrum along with the DOA estimates in both azimuth and elevation. We avoid any explicit feature extraction step by using the magnitude and phase of the spectrogram as input to the network. The proposed DOAnet is evaluated by estimating the DOAs of multiple concurrently present sources in anechoic, matched and unmatched reverberant conditions. The results show that the proposed DOAnet is capable of estimating the number of sources and their respective DOAs with good precision and generate SPS with high signal-to-noise ratio.

Connected through hyperedge HE-1 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "indoor sound source localization with probabilistic 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-2 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-3 (induced by source paper "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"):
  with members:
    - "multichannel sound event detection using 3d convolutional neural networks for learning inter channel features"
    - "deep neural networks for multiple speaker detection and localization"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection using spatial features and 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 "polyphonic sound event detection and localization using a two stage strategy"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "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"
    - "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-5 (induced by source paper "data driven estimation of sinusoid frequencies"):
  with members:
    - "a learning based framework for line spectra super resolution"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"
    - "maximal sparsity with deep networks"
    - "adapting to unknown noise level in sparse deconvolution"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"

Connected through hyperedge HE-6 (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"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "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"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "static visual spatial priors for doa estimation"):
  with members:
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "futuremapping the computational structure of spatial ai systems"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "building machines that learn and think like people"
    - "musan a music speech and noise corpus"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-8 (induced by source paper "localization detection and tracking of multiple moving sound sources with a convolutional recurrent neural network"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "deep neural networks for multiple speaker detection and localization"
    - "audio surveillance a systematic review"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

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)