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: "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"
  Abstract: Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have been used to estimate 'ideal' binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capable of generalizing to the discrimination of voice and non-voice in the context of musical mixtures. Here, we trained a convolutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation of vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for 'karaoke' type applications.

Connected through hyperedge HE-1 (induced by source paper "the sound of motions"):
  with members:
    - "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
    - "self supervised audio visual co segmentation"
    - "learning to localize sound source in visual scenes"
    - "end to end learning of motion representation for video understanding"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"

Connected through hyperedge HE-2 (induced by source paper "co separating sounds of visual objects"):
  with members:
    - "the sound of motions"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"
    - "2 5d visual sound"

Connected through hyperedge HE-3 (induced by source paper "singing voice separation a study on training data"):
  with members:
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "multi scale multi band densenets for audio source separation"
    - "svsgan singing voice separation via generative adversarial network"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi band multi resolution fully convolutional neural networks for singing voice separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "multi resolution fully convolutional neural networks for monaural audio source separation"
    - "adam a method for stochastic optimization"
    - "deep neural networks for single channel source separation"

Connected through hyperedge HE-5 (induced by source paper "learning to separate object sounds by watching unlabeled video"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the sound of pixels"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "learning word like units from joint audio visual analysis"
    - "objects that sound"

Connected through hyperedge HE-6 (induced by source paper "the sound of pixels"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual scene analysis with self supervised multisensory features"
    - "tracking emerges by colorizing videos"
    - "objects that sound"
    - "supervised speech separation based on deep learning an overview"
    - "colorization as a proxy task for visual understanding"

Connected through hyperedge HE-7 (induced by source paper "one deep music representation to rule them all a comparative analysis of different representation learning strategies"):
  with members:
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "look listen and learn"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "transfer learning for music classification and regression tasks"
    - "one model to learn them all"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "fma a dataset for music analysis"

Connected through hyperedge HE-8 (induced by source paper "an overview of lead and accompaniment separation in music"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "multi scale multi band densenets for audio source separation"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "single channel multi speaker separation using deep clustering"
    - "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
    - "discriminative enhancement for single channel audio source separation using deep neural networks"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"

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)