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: "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
  Abstract: Recurrent neural networks have demonstrated to be good at solving prediction problems. However, finding a network that suits a problem is quite hard because of their high sensitivity to the hyperparameter configuration. Automatic hyperparameter optimization methods help to find the most suitable configuration, but they are not extensively adopted because of their high computational cost. In this work, we study the use of the mean absolute error random sampling to compare multiple-hidden-layer architectures and propose an evolutionary strategy-based algorithm that uses its results to optimize the configuration of a recurrent network. We empirically validate our proposal and show that it is possible to predict and compare the expected performance of a hyperparameter configuration in a low-cost way, as well as use these predictions to optimize the configuration of a recurrent network.

Connected through hyperedge HE-1 (induced by source paper "bayesian neural architecture search using a training free performance metric"):
  with members:
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "bin ct urban waste collection based on predicting the container fill level"
    - "low cost recurrent neural network expected performance evaluation"
    - "evolutionary architecture search for deep multitask networks"
    - "metaheuristic design of feedforward neural networks"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "an empirical exploration of deep recurrent connections and memory cells using neuro evolution"):
  with members:
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "from nodes to networks evolving recurrent neural networks"
    - "evolving deep neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "learning simpler language models with the differential state framework"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-3 (induced by source paper "the ant swarm neuro evolution procedure for optimizing recurrent networks"):
  with members:
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "evolving deep neural networks"
    - "learning simpler language models with the differential state framework"
    - "large scale evolution of convolutional neural networks using volunteer computing"
    - "minimal gated unit for recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)