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: "metaheuristic design of feedforward neural networks"
  Abstract: Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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"
    - "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"
    - "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 "neural network architecture search with differentiable cartesian genetic programming for regression"):
  with members:
    - "eat nas elastic architecture transfer for accelerating large scale neural architecture search"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "the quest for the golden activation function"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "efficient architecture search by network transformation"
    - "searching for activation functions"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-3 (induced by source paper "on the bounds of function approximations"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "amc automl for model compression and acceleration on mobile devices"
    - "auto keras an efficient neural architecture search system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "transfer learning with neural automl"

Connected through hyperedge HE-4 (induced by source paper "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"):
  with members:
    - "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"
    - "dlopt deep learning optimization library"
    - "evolving deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "metaheuristic algorithms for convolution neural network"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

Connected through hyperedge HE-5 (induced by source paper "machine learning automation toolbox mlaut"):
  with members:
    - "optimizing deep neural network architecture a tabu search based approach"
    - "training feedforward neural networks with standard logistic activations is feasible"
    - "lets keep it simple using simple architectures to outperform deeper and more complex architectures"
    - "lets keep it simple using simple architectures to outperform deeper architectures"
    - "comparison of 14 different families of classification algorithms on 115 binary datasets"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "caffe convolutional architecture for fast feature embedding"
    - "census data mining and data analysis using weka"

Connected through hyperedge HE-6 (induced by source paper "heuristic design of fuzzy inference systems a review of three decades of research"):
  with members:
    - "multiobjective programming for type 2 hierarchical fuzzy inference trees"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "machine learning approaches to understand the influence of urban environments on human s physiological response"):
  with members:
    - "fruler fuzzy rule learning through evolution for regression"

Connected through hyperedge HE-8 (induced by source paper "neural networks architecture evaluation in a quantum computer"):
  with members:
    - "meta learning evolutionary artificial neural networks"

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)