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 neural network alternative to non negative audio models"
  Abstract: We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation. Due to the extensibility of this approach we show how we can achieve better source separation performance as compared to NMF-based methods, and propose a variety of derivative architectures that can be used for further improvements.

Connected through hyperedge HE-1 (induced by source paper "deep audio prior"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "audio denoising with deep network priors"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "self supervised generation of spatial audio for 360 video"
    - "single image reflection separation with perceptual losses"
    - "does k matter k nn hubness analysis for kernel additive modelling vocal separation"
    - "improving dnn based music source separation using phase features"
    - "wave u net a multi scale neural network for end to end audio source separation"

Connected through hyperedge HE-2 (induced by source paper "end to end non negative autoencoders for sound source separation"):
  with members:
    - "on loss functions for supervised monaural time domain speech enhancement"
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "universal sound separation"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "sdr half baked or well done"
    - "a variational autoencoder for probabilistic non negative matrix factorisation"
    - "end to end source separation with adaptive front ends"

Connected through hyperedge HE-3 (induced by source paper "deep tensor factorization for spatially aware scene decomposition"):
  with members:
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"

Connected through hyperedge HE-4 (induced by source paper "weak label supervision for monaural source separation using non negative denoising variational autoencoders"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "generative adversarial source separation"
    - "learning latent representations for speech generation and transformation"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "structured dropout for weak label and multi instance learning and its application to score informed source separation"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"

Connected through hyperedge HE-5 (induced by source paper "on single channel speech enhancement and on non linear modulation domain kalman filtering"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "modulation domain kalman filtering for monaural blind speech denoising and dereverberation"
    - "on the importance of super gaussian speech priors for machine learning based speech enhancement"
    - "model based speech enhancement in the modulation domain"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"
    - "speech enhancement in multiple noise conditions using deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "audio spectrogram factorization for classification of telephony signals below the auditory threshold"):
  with members:
    - "riddim a rhythm analysis and decomposition tool based on independent subspace analysis"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "semi supervised model training for unbounded conversational speech recognition"

Connected through hyperedge HE-7 (induced by source paper "a variance modeling framework based on variational autoencoders for speech enhancement"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "supervised speech separation based on deep learning an overview"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "tasnet time domain audio separation network for real time single channel speech separation"):
  with members:
    - "speaker independent speech separation with deep attractor network"
    - "end to end source separation with adaptive front ends"
    - "deep attractor network for single microphone speaker separation"
    - "segan speech enhancement generative adversarial network"
    - "single channel multi speaker separation using deep clustering"
    - "identity mappings in deep residual networks"
    - "language modeling with gated convolutional networks"
    - "samplernn an unconditional end to end neural audio generation model"

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)