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: "a report on sound event detection with different binaural features"
  Abstract: In this paper, we compare the performance of using binaural audio features in place of single-channel features for sound event detection. Three different binaural features are studied and evaluated on the publicly available TUT Sound Events 2017 dataset of length 70 minutes. Sound event detection is performed separately with single-channel and binaural features using stacked convolutional and recurrent neural network and the evaluation is reported using standard metrics of error rate and F-score. The studied binaural features are seen to consistently perform equal to or better than the single-channel features with respect to error rate metric.

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"
    - "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"
    - "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-2 (induced by source paper "classical music generation in distinct dastgahs with alimnet acgan"):
  with members:
    - "instrument independent dastgah recognition of iranian classical music using azarnet"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "svsgan singing voice separation via generative adversarial network"
    - "tac gan text conditioned auxiliary classifier generative adversarial network"
    - "neural discrete representation learning"
    - "samplernn an unconditional end to end neural audio generation model"

Connected through hyperedge HE-3 (induced by source paper "on the performance of residual block design alternatives in convolutional neural networks for end to end audio classification"):
  with members:
    - "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "very deep convolutional neural networks for raw waveforms"
    - "identity mappings in deep residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "understanding audio pattern using convolutional neural network from raw waveforms"

Connected through hyperedge HE-4 (induced by source paper "sound event detection with sequentially labelled data based on connectionist temporal classification and unsupervised clustering"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "polyphonic audio tagging with sequentially labelled data using crnn with learnable gated linear units"
    - "language modeling with gated convolutional networks"
    - "audio event detection using weakly labeled data"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "deep multimodal clustering for unsupervised audiovisual learning"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "learning word like units from joint audio visual analysis"
    - "objects that sound"
    - "dynamic routing between capsules"

Connected through hyperedge HE-6 (induced by source paper "multichannel sound event detection using 3d convolutional neural networks for learning inter channel features"):
  with members:
    - "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 event detection in multichannel audio using spatial and harmonic features"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "learning spatiotemporal features with 3d convolutional networks"

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)