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: "sub sampled cubic regularization for non convex optimization"
  Abstract: We consider the minimization of non-convex functions that typically arise in machine learning. Specifically, we focus our attention on a variant of trust region methods known as cubic regularization. This approach is particularly attractive because it escapes strict saddle points and it provides stronger convergence guarantees than first- and second-order as well as classical trust region methods. However, it suffers from a high computational complexity that makes it impractical for large-scale learning. Here, we propose a novel method that uses sub-sampling to lower this computational cost. By the use of concentration inequalities we provide a sampling scheme that gives sufficiently accurate gradient and Hessian approximations to retain the strong global and local convergence guarantees of cubically regularized methods. To the best of our knowledge this is the first work that gives global convergence guarantees for a sub-sampled variant of cubic regularization on non-convex functions. Furthermore, we provide experimental results supporting our theory.

Connected through hyperedge HE-1 (induced by source paper "stochastic newton and cubic newton methods with simple local linear quadratic rates"):
  with members:
    - "sgd general analysis and improved rates"
    - "how good is sgd with random shuffling"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "stochastic variance reduced cubic regularized newton method"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "a proximal stochastic quasi newton algorithm"
    - "variance reduced second order methods"

Connected through hyperedge HE-2 (induced by source paper "ellipsoidal trust region methods for neural network training"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "failures of gradient based deep learning"
    - "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-3 (induced by source paper "finding local minima via stochastic nested variance reduction"):
  with members:
    - "stochastic variance reduced cubic regularized newton method"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "escaping saddles with stochastic gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a generic approach for escaping saddle points"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-4 (induced by source paper "escaping saddles with stochastic gradients"):
  with members:
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a generic approach for escaping saddle points"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"
    - "how to escape saddle points efficiently"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "stochastic variance reduced cubic regularized newton method"):
  with members:
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a general and adaptive robust loss function"
    - "sdca without duality regularization and individual convexity"
    - "matrix completion has no spurious local minimum"
    - "a variance reduced stochastic newton method"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-6 (induced by source paper "clipped matrix completion a remedy for ceiling effects"):
  with members:
    - "low rank methods in event detection"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "matrix completion from noisy entries"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "saving gradient and negative curvature computations finding local minima more efficiently"):
  with members:
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a generic approach for escaping saddle points"
    - "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"
    - "how to escape saddle points efficiently"
    - "faster eigenvector computation via shift and invert preconditioning"

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)