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: "stop wasting my gradients practical svrg"
  Abstract: We present and analyze several strategies for improving the performance of stochastic variance-reduced gradient (SVRG) methods. We first show that the convergence rate of these methods can be preserved under a decreasing sequence of errors in the control variate, and use this to derive variants of SVRG that use growing-batch strategies to reduce the number of gradient calculations required in the early iterations. We further (i) show how to exploit support vectors to reduce the number of gradient computations in the later iterations, (ii) prove that the commonly-used regularized SVRG iteration is justified and improves the convergence rate, (iii) consider alternate mini-batch selection strategies, and (iv) consider the generalization error of the method.

Connected through hyperedge HE-1 (induced by source paper "sample efficient policy gradient methods with recursive variance reduction"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "policy optimization with stochastic mirror descent"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"

Connected through hyperedge HE-2 (induced by source paper "reducing the variance in online optimization by transporting past gradients"):
  with members:
    - "anytime tail averaging"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning dexterous in hand manipulation"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "global convergence of policy gradient methods for linearized control problems"
    - "measuring the effects of data parallelism on neural network training"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (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"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "an improved convergence analysis of stochastic variance reduced policy gradient"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "stochastic variance reduced policy gradient"
    - "policy optimization via importance sampling"
    - "stochastic variance reduction for policy gradient estimation"
    - "stochastic variance reduction methods for policy evaluation"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (induced by source paper "stochastic variance reduction for deep q learning"):
  with members:
    - "reward estimation for variance reduction in deep reinforcement learning"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-6 (induced by source paper "x traincaps accelerated training of capsule nets through lightweight software optimizations"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "dynamic routing between capsules"
    - "adabatch adaptive batch sizes for training deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "starting small learning with adaptive sample sizes"
    - "sgdr stochastic gradient descent with restarts"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "no more pesky learning rate guessing games"

Connected through hyperedge HE-7 (induced by source paper "svrg for policy evaluation with fewer gradient evaluations"):
  with members:
    - "stochastic variance reduced policy gradient"
    - "stochastic variance reduction methods for policy evaluation"
    - "convergent tree backup and retrace with function approximation"
    - "stochastic variance reduction methods for saddle point problems"
    - "on td 0 with function approximation concentration bounds and a centered variant with exponential convergence"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "some limit properties of markov chains induced by stochastic recursive algorithms"):
  with members:
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "regularizing and optimizing lstm language models"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

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)