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: "steerage synthesis of neural networks using architecture search and grow and prune methods"
  Abstract: Neural networks (NNs) have been successfully deployed in many applications. However, architectural design of these models is still a challenging problem. Moreover, neural networks are known to have a lot of redundancy. This increases the computational cost of inference and poses an obstacle to deployment on Internet-of-Thing sensors and edge devices. To address these challenges, we propose the STEERAGE synthesis methodology. It consists of two complementary approaches: efficient architecture search, and grow-and-prune NN synthesis. The first step, covered in a global search module, uses an accuracy predictor to efficiently navigate the architectural search space. The predictor is built using boosted decision tree regression, iterative sampling, and efficient evolutionary search. The second step involves local search. By using various grow-and-prune methodologies for synthesizing convolutional and feed-forward NNs, it reduces the network redundancy, while boosting its performance. We have evaluated STEERAGE performance on various datasets, including MNIST and CIFAR-10. On MNIST dataset, our CNN architecture achieves an error rate of 0.66%, with 8.6x fewer parameters compared to the LeNet-5 baseline. For the CIFAR-10 dataset, we used the ResNet architectures as the baseline. Our STEERAGE-synthesized ResNet-18 has a 2.52% accuracy improvement over the original ResNet-18, 1.74% over ResNet-101, and 0.16% over ResNet-1001, while having comparable number of parameters and FLOPs to the original ResNet-18. This shows that instead of just increasing the number of layers to increase accuracy, an alternative is to use a better NN architecture with fewer layers. In addition, STEERAGE achieves an error rate of just 3.86% with a variant of ResNet architecture with 40 layers. To the best of our knowledge, this is the highest accuracy obtained by ResNet-based architectures on the CIFAR-10 dataset.

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)