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: "probabilistic line searches for stochastic optimization"
  Abstract: In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent.

Connected through hyperedge HE-1 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates"):
  with members:
    - "training neural networks for and by interpolation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "a variational inequality perspective on generative adversarial nets"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an alternative view when does sgd escape local minima"

Connected through hyperedge HE-2 (induced by source paper "deepobs a deep learning optimizer benchmark suite"):
  with members:
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "bda pch block diagonal approximation of positive curvature hessian for training neural networks"
    - "know what you don t know unanswerable questions for squad"
    - "block diagonal hessian free optimization for training neural networks"
    - "tbd benchmarking and analyzing deep neural network training"
    - "attention is all you need"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-3 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing"):
  with members:
    - "oboe collaborative filtering for automl model selection"
    - "convolutional neural networks with alternately updated clique"
    - "learning to branch"
    - "adabatch efficient gradient aggregation rules for sequential and parallel stochastic gradient methods"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning to learn by gradient descent by gradient descent"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "backpack packing more into backprop"):
  with members:
    - "a modular approach to block diagonal hessian approximations for second order optimization methods"
    - "deepobs a deep learning optimizer benchmark suite"
    - "limitations of the empirical fisher approximation"
    - "don t unroll adjoint differentiating ssa form programs"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "not all samples are created equal deep learning with importance sampling"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "coupling adaptive batch sizes with learning rates"

Connected through hyperedge HE-5 (induced by source paper "parabolic approximation line search an efficient and effective line search approach for dnns"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "neither quick nor proper evaluation of quickprop for learning deep neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "accelerating mini batch sarah by step size rules"):
  with members:
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "online learning rate adaptation with hypergradient descent"
    - "importance sampling for minibatches"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "better mini batch algorithms via accelerated gradient methods"

Connected through hyperedge HE-7 (induced by source paper "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"):
  with members:
    - "stagewise safe bayesian optimization with gaussian processes"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "intent aware probabilistic trajectory estimation for collision prediction with uncertainty quantification"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "reachable set approach to collision avoidance for uavs"

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)