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: "adversarial signal denoising with encoder decoder networks"
  Abstract: The presence of noise is common in signal processing independent of the signal type. Deep neural networks have shown good performance in removing signal noise, especially in the image domain. In this work, we consider deep neural networks as a denoising tool where our focus is on one dimensional signals. For that purpose, we introduce an encoder-decoder network architecture to denoise signals, represented by a sequence of measurements. Instead of relying only on the standard reconstruction error to train the encoder-decoder network, we treat the task of signal denoising as distribution alignment between the clean and noisy signals. Then, we propose to train the encoder-decoder with adversarial learning, where the goal is to align the clean and noisy signal latent representation. Unlike standard adversarial learning, we do not have access to the distribution of the clean signal's latent representation in advance. For that reason, we propose a new formulation where both clean and noisy signals pass through the encoder to produce the latent representation. Afterwards, a discriminator neural network has to detect whether the latent representation comes from the clean or noisy signal. At the end of training, aligning the two signal distributions results in removing the noise. In our experiments, we study two signal types with complex noise models. First, we evaluate on electrocardiography and later on motion signal denoising. We show better performance than the related learning-based and non-learning approaches, such as autoencoders, wavenet denoiser, recurrent neural networks and wavelets, demonstrating the benefits of adversarial learning for one dimensional signal denoising.

Connected through hyperedge HE-1 (induced by source paper "few shot meta denoising"):
  with members:
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "unprocessing images for learned raw denoising"
    - "prototypical networks for few shot learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a wavenet for speech denoising"
    - "xunit learning a spatial activation function for efficient image restoration"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

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)