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 metrics for neural networks i feedforward networks"
  Abstract: We describe four algorithms for neural network training, each adapted to different scalability constraints. These algorithms are mathematically principled and invariant under a number of transformations in data and network representation, from which performance is thus independent. These algorithms are obtained from the setting of differential geometry, and are based on either the natural gradient using the Fisher information matrix, or on Hessian methods, scaled down in a specific way to allow for scalability while keeping some of their key mathematical properties.

Connected through hyperedge HE-1 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "fault sneaking attack a stealthy framework for misleading deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "hessian aware zeroth order optimization for black box adversarial attack"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-2 (induced by source paper "limitations of the empirical fisher approximation for natural gradient descent"):
  with members:
    - "information matrices and generalization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "slang fast structured covariance approximations for bayesian deep learning with natural gradient"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three factors influencing minima in sgd"
    - "signsgd compressed optimisation for non convex problems"
    - "noisy natural gradient as variational inference"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"

Connected through hyperedge HE-3 (induced by source paper "neural spectrum alignment"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "kernel and deep regimes in overparametrized models"
    - "asymptotics of wide networks from feynman diagrams"
    - "gradient dynamics of shallow univariate relu networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "general probabilistic surface optimization and log density estimation"

Connected through hyperedge HE-4 (induced by source paper "tangent space separability in feedforward neural networks"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "exploring generalization in deep learning"
    - "comparing information theoretic measures of complexity in boltzmann machines"
    - "sharp minima can generalize for deep nets"
    - "the power of deeper networks for expressing natural functions"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"

Connected through hyperedge HE-5 (induced by source paper "limitations of the empirical fisher approximation"):
  with members:
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "noisy natural gradient as variational inference"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-6 (induced by source paper "a coordinate free construction of scalable natural gradient"):
  with members:
    - "accelerating natural gradient with higher order invariance"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "noisy natural gradient as variational inference"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-7 (induced by source paper "accelerating natural gradient with higher order invariance"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-8 (induced by source paper "fast approximate natural gradient descent in a kronecker factored eigenbasis"):
  with members:
    - "convolutional sequence to sequence learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "adadelta an adaptive learning rate method"

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)