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: "fitness dependent optimizer inspired by the bee swarming reproductive process"
  Abstract: In this paper, a novel swarm intelligent algorithm is proposed, known as the fitness dependent optimizer (FDO). The bee swarming the reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. It is worth mentioning that the FDO is considered a particle swarm optimization (PSO)-based algorithm that updates the search agent position by adding velocity (pace). However, the FDO calculates velocity differently; it uses the problem fitness function value to produce weights, and these weights guide the search agents during both the exploration and exploitation phases. Throughout this paper, the FDO algorithm is presented, and the motivation behind the idea is explained. Moreover, the FDO is tested on a group of 19 classical benchmark test functions, and the results are compared with three well-known algorithms: PSO, the genetic algorithm (GA), and the dragonfly algorithm (DA); in addition, the FDO is tested on the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC-C06, 2019 Competition) [1]. The results are compared with three modern algorithms: (DA), the whale optimization algorithm (WOA), and the salp swarm algorithm (SSA). The FDO results show better performance in most cases and comparative results in other cases. Furthermore, the results are statistically tested with the Wilcoxon rank-sum test to show the significance of the results. Likewise, the FDO stability in both the exploration and exploitation phases is verified and performance-proofed using different standard measurements. Finally, the FDO is applied to real-world applications as evidence of its feasibility.

Connected through hyperedge HE-1 (induced by source paper "improved fitness dependent optimizer algorithm"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"
    - "a comprehensive study on pedestrians evacuation"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "a multi hidden recurrent neural network with a modified grey wolf optimizer"
    - "dragonfly algorithm and its applications in applied science survey"
    - "a simulation model for pedestrian crowd evacuation based on various ai techniques"

Connected through hyperedge HE-2 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "a multi hidden recurrent neural network with a modified grey wolf optimizer"
    - "dragonfly algorithm and its applications in applied science survey"

Connected through hyperedge HE-3 (induced by source paper "a survey on dragonfly algorithm and its applications in engineering"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "a multi hidden recurrent neural network with a modified grey wolf optimizer"

Connected through hyperedge HE-4 (induced by source paper "a simulation model for pedestrian crowd evacuation based on various ai techniques"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "a comprehensive study on pedestrians evacuation"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "a multi hidden recurrent neural network with a modified grey wolf optimizer"
    - "netlogo implementation of an evacuation scenario"
    - "modelling reactive and proactive behaviour in simulation a case study in a university organisation"

Connected through hyperedge HE-5 (induced by source paper "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "a multi hidden recurrent neural network with a modified grey wolf optimizer"
    - "adaptive evolutionary clustering"
    - "a literature survey of benchmark functions for global optimisation problems"

Connected through hyperedge HE-6 (induced by source paper "dragonfly algorithm and its applications in applied science survey"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "a systematic and meta analysis survey of whale optimization algorithm"

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)