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: "a systematic and meta analysis survey of whale optimization algorithm"
  Abstract: Whale Optimization Algorithm (WOA) is a nature-inspired meta-heuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC, PSO, etc.Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

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"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"

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)