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: "platemo a matlab platform for evolutionary multi objective optimization"
  Abstract: Over the last three decades, a large number of evolutionary algorithms have been developed for solving multiobjective#R##N#optimization problems. However, there lacks an up-to-date and comprehensive software platform for#R##N#researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve#R##N#their real-world problems. The demand of such a common tool becomes even more urgent, when the source code#R##N#of many proposed algorithms has not been made publicly available. To address these issues, we have developed#R##N#a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes#R##N#more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with#R##N#several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users#R##N#to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files.#R##N#More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the#R##N#basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative#R##N#experiments, embedding new algorithms, creating new test problems, and developing performance indicators.#R##N#Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.

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"
    - "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"
    - "a brief review of nature inspired algorithms for optimization"
    - "new hoopoe heuristic optimization"
    - "group leaders optimization algorithm"

Connected through hyperedge HE-2 (induced by source paper "evolutionary multi objective optimization driven by generative adversarial networks gans"):
  with members:
    - "improved regularity model based eda for many objective optimization"
    - "preselection via classification a case study on evolutionary multiobjective optimization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "locating the boundaries of pareto fronts a many objective evolutionary algorithm based on corner solution search"):
  with members:
    - "techniques for highly multiobjective optimisation some nondominated points are better than others"

Connected through hyperedge HE-4 (induced by source paper "what weights work for you adapting weights for any pareto front shape in decomposition based evolutionary multi objective optimisation"):
  with members:
    - "how to read many objective solution sets in parallel coordinates"

Connected through hyperedge HE-5 (induced by source paper "a many objective evolutionary algorithm with angle based selection and shift based density estimation"):
  with members:
    - "techniques for highly multiobjective optimisation some nondominated points are better than others"

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)