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: "dynamic optimization of neural network structures using probabilistic modeling"
  Abstract: Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNNs have been proposed, selecting and designing the appropriate network structure for a target problem is a challenging task. In this paper, we propose a method to simultaneously optimize the network structure and weight parameters during neural network training. We consider a probability distribution that generates network structures, and optimize the parameters of the distribution instead of directly optimizing the network structure. The proposed method can apply to the various network structure optimization problems under the same framework. We apply the proposed method to several structure optimization problems such as selection of layers, selection of unit types, and selection of connections using the MNIST, CIFAR-10, and CIFAR-100 datasets. The experimental results show that the proposed method can find the appropriate and competitive network structures.

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"
    - "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"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "controlling model complexity in probabilistic model based dynamic optimization of neural network structures"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-3 (induced by source paper "an end to end approach for speeding up neural network inference"):
  with members:
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "rethinking the value of network pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "data driven sparse structure selection for deep neural networks"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (induced by source paper "parameterless stochastic natural gradient method for discrete optimization and its application to hyper parameter optimization for neural network"):
  with members:
    - "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"
    - "objective improvement in information geometric optimization"

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.NE(Neural and Evolutionary Computing)