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: "investigating recurrent neural network memory structures using neuro evolution"
  Abstract: This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM, MGU and UGRNN cells. EXAMM evolved RNNs to perform prediction of large-scale, real world time series data from the aviation and power industries. These data sets consist of very long time series (thousands of readings), each with a large number of potentially correlated and dependent parameters. Four different parameters were selected for prediction and EXAMM runs were performed using each memory cell type alone, each cell type with feed forward nodes, and with all possible memory cell types. Evolved RNN performance was measured using repeated k-fold cross validation, resulting in 1210 EXAMM runs which evolved 2,420,000 RNNs in 12,100 CPU hours on a high performance computing cluster. Generalization of the evolved RNNs was examined statistically, providing interesting findings that can help refine the RNN memory cell design as well as inform future neuro-evolution algorithms development.

Connected through hyperedge HE-1 (induced by source paper "bayesian neural architecture search using a training free performance metric"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "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:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "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:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "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)