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: "quality and diversity optimization a unifying modular framework"
  Abstract: The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named Quality-Diversity optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. The contribution of this paper is threefold. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and the Novelty Search with Local Competition), and that highlights the large variety of variants that can be investigated within this family. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of Quality-Diversity algorithms on three different experimental scenarios.

Connected through hyperedge HE-1 (induced by source paper "scaling map elites to deep neuroevolution"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "adversarial policies attacking deep reinforcement learning"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "evolvability es scalable and direct optimization of evolvability"
    - "teacher algorithms for curriculum learning of deep rl in continuously parameterized environments"
    - "reset free trial and error learning for robot damage recovery"

Connected through hyperedge HE-2 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "reset free trial and error learning for robot damage recovery"
    - "on first order meta learning algorithms"
    - "multi objective model based policy search for data efficient learning with sparse rewards"
    - "efficient transfer learning and online adaptation with latent variable models for continuous control"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"

Connected through hyperedge HE-3 (induced by source paper "adaptive prior selection for repertoire based online adaptation in robotics"):
  with members:
    - "dynamics aware unsupervised discovery of skills"
    - "reset free trial and error learning for robot damage recovery"
    - "multi objective model based policy search for data efficient learning with sparse rewards"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "patchwork kriging for large scale gaussian process regression"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-4 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function"):
  with members:
    - "quality diversity through surprise"
    - "evaluating map elites on constrained optimization problems"
    - "precise runtime analysis for plateaus"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "illuminating search spaces by mapping elites"
    - "a tabu search path relinking algorithm to solve the job shop scheduling problem"
    - "novelty search in competitive coevolution"
    - "evaluating las vegas algorithms pitfalls and remedies"

Connected through hyperedge HE-5 (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"
    - "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
    - "robots that can adapt like animals"

Connected through hyperedge HE-6 (induced by source paper "covariance matrix adaptation for the rapid illumination of behavior space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "procedural content generation through quality diversity"
    - "alphastar an evolutionary computation perspective"
    - "evaluating map elites on constrained optimization problems"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "the many ai challenges of hearthstone"
    - "diverse agents for ad hoc cooperation in hanabi"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning and exploration of reachable outcome space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "from exploration to control learning object manipulation skills through novelty search and local adaptation"
    - "reset free trial and error learning for robot damage recovery"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-8 (induced by source paper "procedural content generation through quality diversity"):
  with members:
    - "general video game rule generation"
    - "quality diversity through surprise"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "adaptive music composition for games"
    - "talakat bullet hell generation through constrained map 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)