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: "painless stochastic gradient interpolation line search and convergence rates"
  Abstract: Recent works have shown that stochastic gradient descent (SGD) achieves the fast convergence rates of full-batch gradient descent for over-parameterized models satisfying certain interpolation conditions. However, the step-size used in these works depends on unknown quantities, and SGD's practical performance heavily relies on the choice of the step-size. We propose to use line-search methods to automatically set the step-size when training models that can interpolate the data. We prove that SGD with the classic Armijo line-search attains the fast convergence rates of full-batch gradient descent in convex and strongly-convex settings. We also show that under additional assumptions, SGD with a modified line-search can attain a fast rate of convergence for non-convex functions. Furthermore, we show that a stochastic extra-gradient method with a Lipschitz line-search attains a fast convergence rate for an important class of non-convex functions and saddle-point problems satisfying interpolation. We then give heuristics to use larger step-sizes and acceleration with our line-search techniques. We compare the proposed algorithms against numerous optimization methods for standard classification tasks using both kernel methods and deep networks. The proposed methods are robust and result in competitive performance across all models and datasets. Moreover, for the deep network models, SGD with our line-search results in both faster convergence and better generalization.

Connected through hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "feedback network for image super resolution"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "hamiltonian neural networks"
    - "deep predictive coding network for object recognition"

Connected through hyperedge HE-2 (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"
    - "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"
    - "stochastic cubic regularization for fast nonconvex optimization"

Connected through hyperedge HE-3 (induced by source paper "training neural networks for and by interpolation"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "deep frank wolfe for neural network optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "fast and furious convergence stochastic second order methods under interpolation"):
  with members:
    - "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"
    - "big batch sgd automated inference using adaptive batch sizes"

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)