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: "finito a faster permutable incremental gradient method for big data problems"
  Abstract: Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem. In this work we introduce a new method in this class with a theoretical convergence rate four times faster than existing methods, for sums with sufficiently many terms. This method is also amendable to a sampling without replacement scheme that in practice gives further speed-ups. We give empirical results showing state of the art performance.

Connected through hyperedge HE-1 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization"):
  with members:
    - "variance reduction with sparse gradients"
    - "stochastic newton and cubic newton methods with simple local linear quadratic rates"
    - "sgd general analysis and improved rates"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"

Connected through hyperedge HE-2 (induced by source paper "periodic q learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "target based temporal difference learning"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of minimizing convex finite sums without using the indices of the individual functions"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "information based complexity feedback and dynamics in convex programming"

Connected through hyperedge HE-4 (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"
    - "sub sampled cubic regularization for non convex optimization"
    - "a proximal stochastic quasi newton algorithm"

Connected through hyperedge HE-5 (induced by source paper "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "direct acceleration of saga using sampled negative momentum"
    - "sdca without duality regularization and individual convexity"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "variance reduced stochastic gradient descent with neighbors"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "mini batch primal and dual methods for svms"

Connected through hyperedge HE-6 (induced by source paper "asvrg accelerated proximal svrg"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "accelerated variance reduced stochastic admm"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "mini batch semi stochastic gradient descent in the proximal setting"

Connected through hyperedge HE-7 (induced by source paper "stochastic variance reduced policy gradient"):
  with members:
    - "policy gradient methods for reinforcement learning with function approximation and action dependent baselines"
    - "stochastic variance reduction for policy gradient estimation"
    - "stochastic variance reduction methods for policy evaluation"
    - "stochastic variance reduction methods for saddle point problems"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "benchmarking deep reinforcement learning for continuous control"
    - "stop wasting my gradients practical svrg"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "on the ineffectiveness of variance reduced optimization for deep learning"):
  with members:
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "stochastic variance reduction methods for saddle point problems"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"

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)