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: "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
  Abstract: Abstract   Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals’ physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems.

Connected through hyperedge HE-1 (induced by source paper "improved fitness dependent optimizer algorithm"):
  with members:
    - "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 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:
    - "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 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:
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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:
    - "a comprehensive study on pedestrians evacuation"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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:
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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:
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "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)