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: "practical riemannian neural networks"
  Abstract: We provide the first experimental results on non-synthetic datasets for the quasi-diagonal Riemannian gradient descents for neural networks introduced in [Ollivier, 2015]. These include the MNIST, SVHN, and FACE datasets as well as a previously unpublished electroencephalogram dataset. The quasi-diagonal Riemannian algorithms consistently beat simple stochastic gradient gradient descents by a varying margin. The computational overhead with respect to simple backpropagation is around a factor $2$. Perhaps more interestingly, these methods also reach their final performance quickly, thus requiring fewer training epochs and a smaller total computation time. We also present an implementation guide to these Riemannian gradient descents for neural networks, showing how the quasi-diagonal versions can be implemented with minimal effort on top of existing routines which compute gradients.

Connected through hyperedge HE-1 (induced by source paper "equi normalization of neural networks"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "instance normalization the missing ingredient for fast stylization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "all you need is a good init"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "optimizing neural networks with kronecker factored approximate curvature"

Connected through hyperedge HE-2 (induced by source paper "fisher information and natural gradient learning of random deep networks"):
  with members:
    - "new insights and perspectives on the natural gradient method"
    - "riemannian metrics for neural networks i feedforward networks"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-3 (induced by source paper "diagonal rescaling for neural networks"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-4 (induced by source paper "a riemannian network for spd matrix learning"):
  with members:
    - "matrix neural networks"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "riemannian metrics for neural networks i feedforward networks"
    - "spatio temporal covariance descriptors for action and gesture recognition"
    - "spectral networks and locally connected networks on graphs"

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.NE(Neural and Evolutionary Computing)