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: "linear combination of distance measures for surrogate models in genetic programming"
  Abstract: Surrogate models are a well established approach to reduce the number of expensive function evaluations in continuous optimization. In the context of genetic programming, surrogate modeling still poses a challenge, due to the complex genotype-phenotype relationships. We investigate how different genotypic and phenotypic distance measures can be used to learn Kriging models as surrogates. We compare the measures and suggest to use their linear combination in a kernel. #R##N#We test the resulting model in an optimization framework, using symbolic regression problem instances as a benchmark. Our experiments show that the model provides valuable information. Firstly, the model enables an improved optimization performance compared to a model-free algorithm. Furthermore, the model provides information on the contribution of different distance measures. The data indicates that a phenotypic distance measure is important during the early stages of an optimization run when less data is available. In contrast, genotypic measures, such as the tree edit distance, contribute more during the later stages.

Connected through hyperedge HE-1 (induced by source paper "surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning"):
  with members:
    - "improving neuroevolution efficiency by surrogate model based optimization with phenotypic distance kernels"
    - "distance based kernels for surrogate model based neuroevolution"
    - "data efficient neuroevolution with kernel based surrogate models"

Connected through hyperedge HE-2 (induced by source paper "prediction of neural network performance by phenotypic modeling"):
  with members:
    - "improving neuroevolution efficiency by surrogate model based optimization with phenotypic distance kernels"
    - "data efficient neuroevolution with kernel based surrogate models"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-3 (induced by source paper "improving neuroevolution efficiency by surrogate model based optimization with phenotypic distance kernels"):
  with members:
    - "distance based kernels for surrogate model based neuroevolution"
    - "data efficient neuroevolution with kernel based surrogate models"

Connected through hyperedge HE-4 (induced by source paper "positional cartesian genetic programming"):
  with members:
    - "evolving simple programs for playing atari games"
    - "differentiable genetic programming"

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)