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 brief review of nature inspired algorithms for optimization"
  Abstract: Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very effi cient and thus have become popular tools for solving real-world problems. Some algorithms are insuffici ently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.

Connected through hyperedge HE-1 (induced by source paper "comprehensive taxonomies of nature and bio inspired optimization inspiration versus algorithmic behavior critical analysis and recommendations"):
  with members:
    - "jmetalpy a python framework for multi objective optimization with metaheuristics"
    - "multi species cuckoo search algorithm for global optimization"
    - "platemo a matlab platform for evolutionary multi objective optimization"
    - "across neighborhood search for numerical optimization"
    - "a social spider algorithm for global optimization"
    - "an optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation"
    - "new hoopoe heuristic optimization"
    - "group leaders optimization algorithm"

Connected through hyperedge HE-2 (induced by source paper "mitigating metaphors a comprehensible guide to recent nature inspired algorithms"):
  with members:
    - "learned optimizers that scale and generalize"
    - "a history of metaheuristics"
    - "learning to optimize"

Connected through hyperedge HE-3 (induced by source paper "nature inspired computational model of population desegregation under group leaders influence"):
  with members:
    - "moore and more and symmetry"

Connected through hyperedge HE-4 (induced by source paper "metaheuristic design of feedforward neural networks"):
  with members:
    - "ensemble of heterogeneous flexible neural trees using multiobjective genetic programming"
    - "deep learning in neural networks"
    - "simultaneous optimization of neural network weights and active nodes using metaheuristics"
    - "supervised learning in multilayer spiking neural networks"
    - "analysis of a nature inspired firefly algorithm based back propagation neural network training"
    - "meta learning evolutionary artificial neural networks"
    - "on classes of functions for which no free lunch results hold"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "analysis of microarray data using artificial intelligence based techniques"):
  with members:
    - "formal concept analysis for knowledge discovery from biological data"
    - "recurrent neural network based hybrid model for reconstructing gene regulatory network"
    - "ant colony optimization for inferring key gene interactions"
    - "reconstruction and analysis of cancer specific gene regulatory networks from gene expression profiles"
    - "a comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data"
    - "evolutionary algorithms in genetic regulatory networks model"
    - "a novel anticlustering filtering algorithm for the prediction of genes as a drug target"
    - "artificial neural networks for beginners"

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)