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: "audio denoising with deep network priors"
  Abstract: We present a method for audio denoising that combines processing done in both the time domain and the time-frequency domain. Given a noisy audio clip, the method trains a deep neural network to fit this signal. Since the fitting is only partly successful and is able to better capture the underlying clean signal than the noise, the output of the network helps to disentangle the clean audio from the rest of the signal. The method is completely unsupervised and only trains on the specific audio clip that is being denoised. Our experiments demonstrate favorable performance in comparison to the literature methods, and our code and audio samples are available at https: //github.com/mosheman5/DNP. Index Terms: Audio denoising; Unsupervised learning

Connected through hyperedge HE-1 (induced by source paper "deep audio prior"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "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"
    - "deep attractor network for single microphone speaker separation"

Connected through hyperedge HE-2 (induced by source paper "algorithmic guarantees for inverse imaging with untrained network priors"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "deep compressed sensing"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "one dimensional deep image prior for time series inverse problems"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep ptych subsampled fourier ptychography using generative priors"

Connected through hyperedge HE-3 (induced by source paper "j net randomly weighted u net for audio source separation"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "progressive image deraining networks a better and simpler baseline"
    - "convolutional recurrent neural network based progressive learning for monaural speech enhancement"
    - "randomly weighted cnns for music audio classification"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"
    - "unpaired image to image translation using cycle consistent adversarial 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)