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: "layer wise training of deep networks using kernel similarity"
  Abstract: Deep learning has shown promising results in many machine learning applications. The hierarchical feature representation built by deep networks enable compact and precise encoding of the data. A kernel analysis of the trained deep networks demonstrated that with deeper layers, more simple and more accurate data representations are obtained. In this paper, we propose an approach for layer-wise training of a deep network for the supervised classification task. A transformation matrix of each layer is obtained by solving an optimization aimed at a better representation where a subsequent layer builds its representation on the top of the features produced by a previous layer. We compared the performance of our approach with a DNN trained using back-propagation which has same architecture as ours. Experimental results on the real image datasets demonstrate efficacy of our approach. We also performed kernel analysis of layer representations to validate the claim of better feature encoding.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous multilayer generalized operational perceptron"):
  with members:
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "progressive learning for systematic design of large neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "ssfn self size estimating feed forward network with low complexity limited need for human intervention and consistent behaviour across trials"):
  with members:
    - "do cifar 10 classifiers generalize to cifar 10"
    - "autoaugment learning augmentation policies from data"
    - "progressive learning for systematic design of large neural networks"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "greedy layerwise learning can scale to imagenet"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "a provably correct algorithm for deep learning that actually works"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "fenchel lifted networks a lagrange relaxation of neural network training"

Connected through hyperedge HE-4 (induced by source paper "operational neural networks"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "progressive operational perceptron with memory"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "greedy layerwise learning can scale to imagenet"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "a provably correct algorithm for deep learning that actually works"
    - "i revnet deep invertible networks"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-6 (induced by source paper "learning multiple levels of representations with kernel machines"):
  with members:
    - "deep forest"
    - "deep forest towards an alternative to deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "learning functions when is deep better than shallow"
    - "a neural algorithm of artistic style"
    - "deep kernel learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)