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 "simple hyper heuristics control the neighbourhood size of randomised local search optimally for leadingones"):
  Abstract: Selection HHs are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of low-level heuristics. A machine learning mechanism is generally used to decide which low-level heuristic should be applied in each decision step. In this paper we analyse whether sophisticated learning mechanisms are always necessary for HHs to perform well. To this end we consider the most simple HHs from the literature and rigorously analyse their performance for the LeadingOnes function. Our analysis shows that the standard Simple Random, Permutation, Greedy and Random Gradient HHs show no signs of learning. While the former HHs do not attempt to learn from the past performance of low-level heuristics, the idea behind the Random Gradient HH is to continue to exploit the currently selected heuristic as long as it is successful. Hence, it is embedded with a reinforcement learning mechanism with the shortest possible memory. However, the probability that a promising heuristic is successful in the next step is relatively low when perturbing a reasonable solution to a combinatorial optimisation problem. We generalise the simple Random Gradient HH so success can be measured over a fixed period of time tau, instead of a single iteration. For LO we prove that the Generalised Random Gradient HH can learn to adapt the neighbourhood size of RLS to optimality during the run. We prove it has the best possible performance achievable with the low-level heuristics. We also prove that the performance of the HH improves as the number of low-level local search heuristics to choose from increases. Finally, we show that the advantages of GRG over RLS and EAs using standard bit mutation increase if the anytime performance is considered. Experimental analyses confirm these results for different problem sizes.

Includes members:
  Member "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards white box benchmarks for algorithm control")
  Member "self adjusting mutation rates with provably optimal success rules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates")
  Member "better runtime guarantees via stochastic domination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "significance based estimation of distribution algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "working principles of binary differential evolution")
  Member "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational complexity analysis of genetic programming")
  Member "the 1 1 elitist black box complexity of leadingones":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler")
  Member "level based analysis of genetic algorithms and other search processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "algorithm selection for combinatorial search problems a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lossless compression of deep neural networks")

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)