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: "indoor sound source localization with probabilistic neural network"
  Abstract: It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization (SSL). To address this challenge, in this paper, we propose an SSL algorithm based on a probabilistic neural network, namely a generalized cross-correlation classification algorithm (GCA). Experimental results for adverse environments with high reverberation time    $T_{60}$   up to 600 ms and low SNR such as -10 dB show that the average azimuth angle error and elevation angle error by GCA are only 4.6° and 3.1°, respectively. Compared with three recently published algorithms, GCA has increased the success rate on direction of arrival estimation significantly with good robustness to environmental changes. These results show that the proposed GCA can localize accurately and robustly for diverse indoor applications where the site acoustic features can be studied prior to the localization stage.

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"
    - "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-2 (induced by source paper "watch listen and tell multi modal weakly supervised dense event captioning"):
  with members:
    - "an attempt towards interpretable audio visual video captioning"
    - "learning to localize sound source in visual scenes"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "weakly supervised dense event captioning in videos"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-3 (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"
    - "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 "end to end binaural sound localisation from the raw waveform"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "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-5 (induced by source paper "towards end to end acoustic localization using deep learning from audio signal to source position coordinates"):
  with members:
    - "a geometrical statistical approach to outlier removal for tdoa measurements"
    - "tdoa matrices algebraic properties and their application to robust denoising with missing data"

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)