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: "riemannian approach to batch normalization"
  Abstract: Batch Normalization (BN) has proven to be an effective algorithm for deep neural network training by normalizing the input to each neuron and reducing the internal covariate shift. The space of weight vectors in the BN layer can be naturally interpreted as a Riemannian manifold, which is invariant to linear scaling of weights. Following the intrinsic geometry of this manifold provides a new learning rule that is more efficient and easier to analyze. We also propose intuitive and effective gradient clipping and regularization methods for the proposed algorithm by utilizing the geometry of the manifold. The resulting algorithm consistently outperforms the original BN on various types of network architectures and datasets.

Connected through hyperedge HE-1 (induced by source paper "an internal covariate shift bounding algorithm for deep neural networks by unitizing layers outputs"):
  with members:
    - "group normalization"
    - "decorrelated batch normalization"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "mask r cnn"
    - "instance normalization the missing ingredient for fast stylization"
    - "identity mappings in deep residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform"):
  with members:
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "riemannian adaptive optimization methods"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "dizzyrnn reparameterizing recurrent neural networks for norm preserving backpropagation"
    - "wide residual networks"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "four things everyone should know to improve batch normalization"):
  with members:
    - "truth or backpropaganda an empirical investigation of deep learning theory"
    - "fixup initialization residual learning without normalization"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "differentiable learning to normalize via switchable normalization"

Connected through hyperedge HE-4 (induced by source paper "fine grained optimization of deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "understanding generalization and optimization performance of deep cnns"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "exploring generalization in deep learning"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "an exponential learning rate schedule for deep learning"):
  with members:
    - "sgd general analysis and improved rates"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "fix your classifier the marginal value of training the last weight layer"
    - "group normalization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "three mechanisms of weight decay regularization"
    - "understanding batch normalization"
    - "scaling sgd batch size to 32k for imagenet training"

Connected through hyperedge HE-6 (induced by source paper "adaptive stochastic gradient algorithms on riemannian manifolds"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-7 (induced by source paper "boosting cnn beyond label in inverse problems"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "spectral normalization for generative adversarial networks"
    - "k space deep learning for accelerated mri"
    - "deep back projection networks for super resolution"
    - "training deep learning based denoisers without ground truth data"
    - "framing u net via deep convolutional framelets application to sparse view ct"

Connected through hyperedge HE-8 (induced by source paper "riemannian adaptive stochastic gradient algorithms on matrix manifolds"):
  with members:
    - "on the convergence of adam and beyond"
    - "shampoo preconditioned stochastic tensor optimization"
    - "geomstats a python package for riemannian geometry in machine learning"
    - "a dual framework for low rank tensor completion"
    - "poincar e embeddings for learning hierarchical representations"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "adaptive bound optimization for online convex optimization"

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)