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: "objective improvement in information geometric optimization"
  Abstract: Information-Geometric Optimization (IGO) is a unified framework of stochastic algorithms for optimization problems. Given a family of probability distributions, IGO turns the original optimization problem into a new maximization problem on the parameter space of the probability distributions. IGO updates the parameter of the probability distribution along the natural gradient, taken with respect to the Fisher metric on the parameter manifold, aiming at maximizing an adaptive transform of the objective function. IGO recovers several known algorithms as particular instances: for the family of Bernoulli distributions IGO recovers PBIL, for the family of Gaussian distributions the pure rank-mu CMA-ES update is recovered, and for exponential families in expectation parametrization the cross-entropy/ML method is recovered. This article provides a theoretical justification for the IGO framework, by proving that any step size not greater than 1 guarantees monotone improvement over the course of optimization, in terms of q-quantile values of the objective function f. The range of admissible step sizes is independent of f and its domain. We extend the result to cover the case of different step sizes for blocks of the parameters in the IGO algorithm. Moreover, we prove that expected fitness improves over time when fitness-proportional selection is applied, in which case the RPP algorithm is recovered.

Connected through hyperedge HE-1 (induced by source paper "adaptive stochastic natural gradient method for one shot neural architecture search"):
  with members:
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "dynamic optimization of neural network structures using probabilistic modeling"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-2 (induced by source paper "parameterless stochastic natural gradient method for discrete optimization and its application to hyper parameter optimization for neural network"):
  with members:
    - "dynamic optimization of neural network structures using probabilistic modeling"
    - "efficient neural architecture search via parameter sharing"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "learning neural network architectures using backpropagation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "first order and second order variants of the gradient descent a unified framework"):
  with members:
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "policy search in continuous action domains an overview"
    - "trust region policy optimization"
    - "new insights and perspectives on the natural gradient method"
    - "riemannian metrics for neural networks i feedforward networks"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-4 (induced by source paper "lm cma an alternative to l bfgs for large scale black box optimization"):
  with members:
    - "maximum likelihood based online adaptation of hyper parameters in cma es"
    - "a computationally efficient limited memory cma es for large scale optimization"
    - "linear convergence of comparison based step size adaptive randomized search via stability of markov chains"
    - "self adaptive surrogate assisted covariance matrix adaptation evolution strategy"
    - "a linear time natural evolution strategy for non separable functions"

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)