human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "ensemble of heterogeneous flexible neural trees using multiobjective genetic programming":
    also connected through other hyperedges:
      (none)
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks")
  Member "simultaneous optimization of neural network weights and active nodes using metaheuristics":
    also connected through other hyperedges:
      (none)
  Member "a brief review of nature inspired algorithms for optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "comprehensive taxonomies of nature and bio inspired optimization inspiration versus algorithmic behavior critical analysis and recommendations")
  Member "supervised learning in multilayer spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule")
  Member "analysis of a nature inspired firefly algorithm based back propagation neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simplified firefly algorithm for 2d image key points search")
  Member "meta learning evolutionary artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aol adaptive online learning for human trajectory prediction in dynamic video scenes")
  Member "on classes of functions for which no free lunch results hold":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when and why metaheuristics researchers can ignore no free lunch theorems")

Question: Please tell me which class the source paper 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)