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: "joint optimization of masks and deep recurrent neural networks for monaural source separation"
  Abstract: Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including speech separation, singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30-4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30-2.48 dB GNSDR gain and 4.32-5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.

Connected through hyperedge HE-1 (induced by source paper "source separation with deep generative priors"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "music source separation in the waveform domain"
    - "end to end music source separation is it possible in the waveform domain"
    - "single image reflection separation with perceptual losses"
    - "neural separation of observed and unobserved distributions"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis"):
  with members:
    - "audio source separation using variational autoencoders and weak class supervision"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "universal sound separation"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "panns large scale pretrained audio neural networks for audio pattern recognition"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "multi scale multi band densenets for audio source separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-3 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-4 (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-5 (induced by source paper "two step sound source separation training on learned latent targets"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "universal sound separation"
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "sdr half baked or well done"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"

Connected through hyperedge HE-6 (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-7 (induced by source paper "mitas a compressed time domain audio separation network with parameter sharing"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "albert a lite bert for self supervised learning of language representations"
    - "speaker independent speech separation with deep attractor network"
    - "deep attractor network for single microphone speaker separation"
    - "supervised speech separation based on deep learning an overview"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-8 (induced by source paper "modeling the comb filter effect and interaural coherence for binaural source separation"):
  with members:
    - "acoustic reflector localization novel image source reversion and direct localization methods"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "raking the cocktail party"
    - "audio surveillance a systematic review"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

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)