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 "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function"):
  Abstract: Under Frequency Fitness Assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in fitness assignment steps and is subject to minimization. FFA renders optimization processes invariant under bijective transformations of the objective function. This is the strongest invariance property of any optimization procedure to our knowledge. On TwoMax, Jump, and Trap functions of scale s, a (1+1)-EA with standard mutation at rate 1/s can have expected running times exponential in s. In our experiments, a (1+1)-FEA, the same algorithm but using FFA, exhibits mean running times quadratic in s. Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three. On the LeadingOnes and Plateau problems, it seems to be slower than the (1+1)-EA by a factor linear in s. The (1+1)-FEA performs much better than the (1+1)-EA on W-Model and MaxSat instances. Due to the bijection invariance, the behavior of an optimization algorithm using FFA does not change when the objective values are encrypted. We verify this by applying the Md5 checksum computation as transformation to some of the above problems and yield the same behaviors. Finally, FFA can improve the performance of a Memetic Algorithm for Job Shop Scheduling.

Includes members:
  Member "quality diversity through surprise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "procedural content generation through quality diversity")
  Member "evaluating map elites on constrained optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "covariance matrix adaptation for the rapid illumination of behavior space")
  Member "precise runtime analysis for plateaus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost")
  Member "towards a more practice aware runtime analysis of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates")
  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")
  Member "a tabu search path relinking algorithm to solve the job shop scheduling problem":
    also connected through other hyperedges:
      (none)
  Member "novelty search in competitive coevolution":
    also connected through other hyperedges:
      (none)

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)