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: "saturating auto encoders"
  Abstract: We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We show that the saturation regularizer explicitly limits the SATAE's ability to reconstruct inputs which are not near the data manifold. Furthermore, we show that a wide variety of features can be learned when different activation functions are used. Finally, connections are established with the Contractive and Sparse Auto-Encoders.

Connected through hyperedge HE-1 (induced by source paper "accelerating training of deep neural networks with a standardization loss"):
  with members:
    - "group normalization"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "progressive growing of gans for improved quality stability and variation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"

Connected through hyperedge HE-2 (induced by source paper "unsupervised feature learning from temporal data"):
  with members:
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of spatiotemporally coherent metrics"):
  with members:
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "low rank approximations for conditional feedforward computation in deep neural networks"):
  with members:
    - "deep learning of representations looking forward"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "predicting parameters in deep learning"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "building high level features using large scale unsupervised learning"

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)