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: "broadband doa estimation using convolutional neural networks trained with noise signals"
  Abstract: A convolution neural network (CNN) based classification method for broadband DOA estimation is proposed, where the phase component of the short-time Fourier transform coefficients of the received microphone signals are directly fed into the CNN and the features required for DOA estimation are learned during training. Since only the phase component of the input is used, the CNN can be trained with synthesized noise signals, thereby making the preparation of the training data set easier compared to using speech signals. Through experimental evaluation, the ability of the proposed noise trained CNN framework to generalize to speech sources is demonstrated. In addition, the robustness of the system to noise, small perturbations in microphone positions, as well as its ability to adapt to different acoustic conditions is investigated using experiments with simulated and real data.

Connected through hyperedge HE-1 (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"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "sound source localization in a multipath environment using convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "sound source localization in a multipath environment using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "data driven estimation of sinusoid frequencies"):
  with members:
    - "a learning based framework for line spectra super resolution"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "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-4 (induced by source paper "a learning based framework for line spectra super resolution"):
  with members:
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "maximal sparsity with deep networks"
    - "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"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-5 (induced by source paper "machine learning based high resolution doa measurement and robust dm for hybrid analog digital massive mimo transceiver"):
  with members:
    - "robust secure transmission of using main lobe integration based leakage beamforming in directional modulation mu mimo systems"
    - "secure swipt for directional modulation aided af relaying networks"
    - "hybrid analog and digital beamforming for mmwave ofdm large scale antenna arrays"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "transmit energy focusing for doa estimation in mimo radar with colocated antennas"

Connected through hyperedge HE-6 (induced by source paper "a report on sound event detection with different binaural features"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "audio surveillance a systematic review"

Connected through hyperedge HE-7 (induced by source paper "sound source localization in a multipath environment using convolutional neural networks"):
  with members:
    - "convolutional neural networks for passive monitoring of a shallow water environment using a single sensor"
    - "hyperspectral cnn classification with limited training samples"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"):
  with members:
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "sound event detection using spatial features and convolutional recurrent neural network"

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)