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 "exploration and exploitation in symbolic regression using quality diversity and evolutionary strategies algorithms"):
  Abstract: By combining Genetic Programming, MAP-Elites and Covariance Matrix Adaptation Evolution Strategy, we demonstrate very high success rates in Symbolic Regression problems. MAP-Elites is used to improve exploration while preserving diversity and avoiding premature convergence and bloat. Then, a Covariance Matrix Adaptation-Evolution Strategy is used to evaluate free scalars through a non-gradient-based black-box optimizer. Although this evaluation approach is not computationally scalable to high dimensional problems, our algorithm is able to find exactly most of the $31$ targets extracted from the literature on which we evaluate it.

Includes members:
  Member "quality diversity through surprise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function")
  Member "a greedy search tree heuristic for symbolic regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interaction transformation evolutionary algorithm for symbolic regression")
  Member "analysing symbolic regression benchmarks under a meta learning approach":
    also connected through other hyperedges:
      (none)
  Member "how noisy data affects geometric semantic genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysing symbolic regression benchmarks under a meta learning approach")
  Member "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quality diversity for multi task optimization")
  Member "quality and diversity optimization a unifying modular framework":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling map elites to deep neuroevolution")
  Member "illuminating search spaces by mapping elites":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")

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)