human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "quality diversity for multi task optimization"):
  Abstract: Quality Diversity (QD) algorithms are a recent family of optimization algorithms that search for a large set of diverse but high-performing solutions. Interestingly, they can solve multiple tasks at once. For instance, they can find the joint positions required for a robotic arm to reach a set of points, which can also be solved by running a classic optimizer for each target point. However, they cannot solve multiple tasks when the fitness needs to be evaluated independently for each task (e.g., optimizing policies to grasp many different objects). In this paper, we propose an extension of the MAP-Elites algorithm, called Multi-task MAP-Elites, that solves multiple tasks when the fitness function depends on the task. We evaluate it on a simulated parametrized planar arm (10-dimensional search space; 5000 tasks) and on a 6-legged robot with legs of different lengths (36-dimensional search space; 2000 tasks). The results show that in both cases our algorithm outperforms the optimization of each task separately with the CMA-ES algorithm.

Includes members:
  Member "adaptive prior selection for repertoire based online adaptation in robotics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors")
  Member "procedural content generation through quality diversity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "reset free trial and error learning for robot damage recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling map elites to deep neuroevolution")
  Member "discovering the elite hypervolume by leveraging interspecies correlation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automating representation discovery with map elites")
  Member "bayesian optimization with automatic prior selection for data efficient direct policy search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors")
  Member "evolutionary multitasking for single objective continuous optimization benchmark problems performance metric and baseline results":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simultaneously evolving deep reinforcement learning models using multifactorial optimization")
  Member "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploration and exploitation in symbolic regression using quality diversity and evolutionary strategies algorithms")
  Member "quality and diversity optimization a unifying modular framework":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling map elites to deep neuroevolution")

Question: Please tell me which class the source paper 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)