human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blank language models")
  Member "adversarial network compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "residual knowledge distillation")
  Member "denoising adversarial autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "man in the middle attacks against machine learning classifiers via malicious generative models")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "a wavenet for speech denoising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian neural networks with maximum mean discrepancy regularization")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blur noise and compression robust generative adversarial networks")

Question: Please tell me which class the source paper 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)