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: "global linear convergence of newton s method without strong convexity or lipschitz gradients"
  Abstract: We show that Newton's method converges globally at a linear rate for objective functions whose Hessians are stable. This class of problems includes many functions which are not strongly convex, such as logistic regression. Our linear convergence result is (i) affine-invariant, and holds even if an (ii) approximate Hessian is used, and if the subproblems are (iii) only solved approximately. Thus we theoretically demonstrate the superiority of Newton's method over first-order methods, which would only achieve a sublinear $O(1/t^2)$ rate under similar conditions.

Connected through hyperedge HE-1 (induced by source paper "fast and furious convergence stochastic second order methods under interpolation"):
  with members:
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "beyond least squares fast rates for regularized empirical risk minimization through self concordance"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "on the acceleration of l bfgs with second order information and stochastic batches"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "adaptive newton method for empirical risk minimization to statistical accuracy"
    - "understanding deep learning requires rethinking generalization"
    - "big batch sgd automated inference using adaptive batch sizes"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "accelerating gradient boosting machine"):
  with members:
    - "proximal boosting and its acceleration"
    - "randomized gradient boosting machine"
    - "accelerated proximal boosting"
    - "xgboost a scalable tree boosting system"
    - "linear coupling an ultimate unification of gradient and mirror descent"
    - "scikit learn machine learning in python"
    - "a primal dual convergence analysis of boosting"

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)