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: "stochastic variance reduced cubic regularized newton method"
  Abstract: We propose a stochastic variance-reduced cubic regularized Newton method for non-convex optimization. At the core of our algorithm is a novel semi-stochastic gradient along with a semi-stochastic Hessian, which are specifically designed for cubic regularization method. We show that our algorithm is guaranteed to converge to an $(\epsilon,\sqrt{\epsilon})$-approximately local minimum within $\tilde{O}(n^{4/5}/\epsilon^{3/2})$ second-order oracle calls, which outperforms the state-of-the-art cubic regularization algorithms including subsampled cubic regularization. Our work also sheds light on the application of variance reduction technique to high-order non-convex optimization methods. Thorough experiments on various non-convex optimization problems support 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"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "sub sampled cubic regularization for non convex optimization"
    - "a proximal stochastic quasi newton algorithm"
    - "variance reduced second order methods"

Connected through hyperedge HE-2 (induced by source paper "finding local minima via stochastic nested variance reduction"):
  with members:
    - "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"
    - "sub sampled cubic regularization for non convex optimization"
    - "how to escape saddle points efficiently"

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)