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 "simplified firefly algorithm for 2d image key points search"):
  Abstract: In order to identify an object, human eyes firstly search the field of view for points or areas which have particular properties. These properties are used to recognise an image or an object. Then this process could be taken as a model to develop computer algorithms for images identification. This paper proposes the idea of applying the simplified firefly algorithm to search for key-areas in 2D images. For a set of input test images the proposed version of firefly algorithm has been examined. Research results are presented and discussed to show the efficiency of this evolutionary computation method.

Includes members:
  Member "a cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an agent driven semantical identifier using radial basis neural networks and reinforcement learning")
  Member "using modularity metrics to assist move method refactoring of large systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an agent driven semantical identifier using radial basis neural networks and reinforcement learning")
  Member "a hybrid neurowavelet predictor for qos control and stability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an agent driven semantical identifier using radial basis neural networks and reinforcement learning")
  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 "metaheuristic design of feedforward neural networks")

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)