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 "improved fitness dependent optimizer algorithm"):
  Abstract: The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor (wf), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs wf randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.

Includes members:
  Member "donkey and smuggler optimization algorithm a collaborative working approach to path finding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation")
  Member "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation")
  Member "a comprehensive study on pedestrians evacuation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simulation model for pedestrian crowd evacuation based on various ai techniques")
  Member "fitness dependent optimizer inspired by the bee swarming reproductive process":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation")
  Member "a systematic and meta analysis survey of whale optimization algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation")
  Member "a multi hidden recurrent neural network with a modified grey wolf optimizer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation")
  Member "dragonfly algorithm and its applications in applied science survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation")
  Member "a simulation model for pedestrian crowd evacuation based on various ai techniques":
    also connected through other hyperedges:
      (none)

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)