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 detection in multichannel audio using spatial and harmonic features"
  Abstract: In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task. Real life sound recordings typically have many overlapping sound events, making it hard to recognize with just mono channel audio. Human listeners have been successfully recognizing the mixture of overlapping sound events using pitch cues and exploiting the stereo (multichannel) audio signal available at their ears to spatially localize these events. Traditionally SED systems have only been using mono channel audio, motivated by the human listener we propose to extend them to use multichannel audio. The proposed SED system is compared against the state of the art mono channel method on the development subset of TUT sound events detection 2016 database. The usage of spatial and harmonic features are shown to improve the performance of SED.

Connected through hyperedge HE-1 (induced by source paper "non negative matrix factorization convolutional neural network nmf cnn for sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "hodgepodge sound event detection based on ensemble of semi supervised learning methods"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-2 (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"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"

Connected through hyperedge HE-3 (induced by source paper "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"):
  with members:
    - "a closer look at weak label learning for audio events"
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "audio set classification with attention model a probabilistic perspective"
    - "reducing model complexity for dnn based large scale audio classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-4 (induced by source paper "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "yolo9000 better faster stronger"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"

Connected through hyperedge HE-5 (induced by source paper "multichannel sound event detection using 3d convolutional neural networks for learning inter channel features"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "mixup based acoustic scene classification using multi channel convolutional neural network"):
  with members:
    - "mixup beyond empirical risk minimization"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "label tree embeddings for acoustic scene classification"
    - "classifying variable length audio files with all convolutional networks and masked global pooling"
    - "xception deep learning with depthwise separable convolutions"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "large scale weakly labeled semi supervised sound event detection in domestic environments"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "unsupervised learning of semantic audio representations"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "an approach for self training audio event detectors using web data"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

Connected through hyperedge HE-8 (induced by source paper "end to end polyphonic sound event detection using convolutional recurrent neural networks with learned time frequency representation input"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "comparing time and frequency domain for audio event recognition using deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

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)