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: "scheduled denoising autoencoders"
  Abstract: We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is only slightly corrupted, the network tends to learn fine-grained features. This motivates the scheduled denoising autoencoder, which starts with a high level of noise that lowers as training progresses. We find that the resulting representation yields a significant boost on a later supervised task compared to the original input, or to a standard denoising autoencoder trained at a single noise level. After supervised fine-tuning our best model achieves the lowest ever reported error on the CIFAR-10 data set among permutation-invariant methods.

Connected through hyperedge HE-1 (induced by source paper "generative modeling by estimating gradients of the data distribution"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "sliced score matching a scalable approach to density and score estimation"
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "autoregressive quantile networks for generative modeling"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "autoencoder based articulatory to acoustic mapping for ultrasound silent speech interfaces"):
  with members:
    - "deep convolutional autoencoder based lossy image compression"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "stylebank an explicit representation for neural image style transfer"
    - "lipreading with long short term memory"
    - "compressing word embeddings"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-3 (induced by source paper "on the exact relationship between the denoising function and the data distribution"):
  with members:
    - "what regularized auto encoders learn from the data generating distribution"

Connected through hyperedge HE-4 (induced by source paper "vconv dae deep volumetric shape learning without object labels"):
  with members:
    - "deep shading convolutional neural networks for screen space shading"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "unsupervised learning of 3d structure from images"
    - "novel views of objects from a single image"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "dense human body correspondences using convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "a curriculum learning method for improved noise robustness in automatic speech recognition"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "speech recognition with deep recurrent neural 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.LG(Machine Learning)