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: "neural separation of observed and unobserved distributions"
  Abstract: Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separation---to tackle the scenario of extracting a signal from an unobserved distribution additively mixed with a signal from an observed distribution. Our method iteratively learns to separate the known distribution from progressively finer estimates of the unknown distribution. In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce Latent Mixture Masking which ensures a good initialization. Extensive experiments on audio and image separation tasks show that our method outperforms current methods that use the same level of supervision, and often achieves similar performance to full supervision.

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"
    - "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"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-2 (induced by source paper "semi supervised monaural singing voice separation with a masking network trained on synthetic mixtures"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "generative adversarial source separation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a theory of output side unsupervised domain adaptation"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

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.LG(Machine Learning)