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: "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
  Abstract: The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.   The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.

Connected through hyperedge HE-1 (induced by source paper "quality diversity for multi task optimization"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "procedural content generation through quality diversity"
    - "reset free trial and error learning for robot damage recovery"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "evolutionary multitasking for single objective continuous optimization benchmark problems performance metric and baseline results"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"

Connected through hyperedge HE-2 (induced by source paper "exploration and exploitation in symbolic regression using quality diversity and evolutionary strategies algorithms"):
  with members:
    - "quality diversity through surprise"
    - "a greedy search tree heuristic for symbolic regression"
    - "analysing symbolic regression benchmarks under a meta learning approach"
    - "how noisy data affects geometric semantic genetic programming"
    - "quality and diversity optimization a unifying modular framework"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-3 (induced by source paper "quality diversity through surprise"):
  with members:
    - "surprise search for evolutionary divergence"
    - "quality and diversity optimization a unifying modular framework"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-4 (induced by source paper "data efficient neuroevolution with kernel based surrogate models"):
  with members:
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "on the relationship between the openai evolution strategy and stochastic gradient descent"
    - "black box data efficient policy search for robotics"
    - "robots that can adapt like animals"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "discovering the elite hypervolume by leveraging interspecies correlation"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "map based multi policy reinforcement learning enhancing adaptability of robots by deep reinforcement learning"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-6 (induced by source paper "hierarchical behavioral repertoires with unsupervised descriptors"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "magan margin adaptation for generative adversarial networks"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "evolving a behavioral repertoire for a walking robot"

Connected through hyperedge HE-7 (induced by source paper "multi objective analysis of map elites performance"):
  with members:
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "evolving a behavioral repertoire for a walking robot"

Connected through hyperedge HE-8 (induced by source paper "prototype discovery using quality diversity"):
  with members:
    - "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)