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: "toward the coevolution of novel vertical axis wind turbines"
  Abstract: The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world’s energy supply mix, but remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. Initially, a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.

Connected through hyperedge HE-1 (induced by source paper "towards an evolvable cancer treatment simulator"):
  with members:
    - "on design mining coevolution and surrogate models"
    - "dynamic multi objectives optimization with a changing number of objectives"
    - "design mining interacting wind turbines"

Connected through hyperedge HE-2 (induced by source paper "comparing direct and indirect representations for environment specific robot component design"):
  with members:
    - "towards the targeted environment specific evolution of robot components"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"

Connected through hyperedge HE-3 (induced by source paper "geodesic convolutional shape optimization"):
  with members:
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "template based monocular 3d shape recovery using laplacian meshes"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "turning high dimensional optimization into computationally expensive optimization"):
  with members:
    - "negatively correlated search"
    - "recursive decomposition for nonconvex optimization"
    - "scaling up estimation of distribution algorithms for continuous optimization"

Connected through hyperedge HE-5 (induced by source paper "on design mining coevolution and surrogate models"):
  with members:
    - "design mining interacting wind turbines"
    - "towards the evolution of vertical axis wind turbines using supershapes"

Connected through hyperedge HE-6 (induced by source paper "design mining microbial fuel cell cascades"):
  with members:
    - "on design mining coevolution and surrogate models"
    - "design mining interacting wind turbines"
    - "towards the evolution of vertical axis wind turbines using supershapes"

Connected through hyperedge HE-7 (induced by source paper "design mining interacting wind turbines"):
  with members:
    - "towards the evolution of vertical axis wind turbines using supershapes"

Connected through hyperedge HE-8 (induced by source paper "design mining vawt wind farms"):
  with members:
    - "design mining interacting wind turbines"
    - "towards the evolution of vertical axis wind turbines using supershapes"

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)