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 "comprehensive taxonomies of nature and bio inspired optimization inspiration versus algorithmic behavior critical analysis and recommendations"):
  Abstract: In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.

Includes members:
  Member "jmetalpy a python framework for multi objective optimization with metaheuristics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pymoo multi objective optimization in python")
  Member "multi species cuckoo search algorithm for global optimization":
    also connected through other hyperedges:
      (none)
  Member "platemo a matlab platform for evolutionary multi objective optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evolutionary multi objective optimization driven by generative adversarial networks gans")
  Member "across neighborhood search for numerical optimization":
    also connected through other hyperedges:
      (none)
  Member "a social spider algorithm for global optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "heuristic optimization of electrical energy systems refined metrics to compare the solutions")
  Member "an optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation":
    also connected through other hyperedges:
      (none)
  Member "a brief review of nature inspired algorithms for optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mitigating metaphors a comprehensible guide to recent nature inspired algorithms")
  Member "new hoopoe heuristic optimization":
    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.AI(Artificial Intelligence)