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: "multi objective analysis of map elites performance"
  Abstract: In certain complex optimization tasks, it becomes necessary to use multiple measures to characterize the performance of different algorithms. This paper presents a method that combines ordinal effect sizes with Pareto dominance to analyze such cases. Since the method is ordinal, it can also generalize across different optimization tasks even when the performance measurements are differently scaled. Through a case study, we show that this method can discover and quantify relations that would be difficult to deduce using a conventional measure-by-measure analysis. This case study applies the method to the evolution of robot controller repertoires using the MAP-Elites algorithm. Here, we analyze the search performance across a large set of parametrizations; varying mutation size and operator type, as well as map resolution, across four different robot morphologies. We show that the average magnitude of mutations has a bigger effect on outcomes than their precise distributions.

Connected through hyperedge HE-1 (induced by source paper "evaluating map elites on constrained optimization problems"):
  with members:
    - "benchmarking evolutionary algorithms for single objective real valued constrained optimization a critical review"
    - "optimisation and illumination of a real world workforce scheduling and routing application via map elites"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "enhanced optimization with composite objectives and novelty selection"
    - "quality and diversity optimization a unifying modular framework"
    - "memetic viability evolution for constrained optimization"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"

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)