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: "onemax in black box models with several restrictions"
  Abstract: Black-box complexity studies lower bounds for the efficiency of general-purpose black-box optimization algorithms such as evolutionary algorithms and other search heuristics. Different models exist, each one being designed to analyze a different aspect of typical heuristics such as the memory size or the variation operators in use. While most of the previous works focus on one particular such aspect, we consider in this work how the combination of several algorithmic restrictions influence the black-box complexity. Our testbed are so-called OneMax functions, a classical set of test functions that is intimately related to classic coin-weighing problems and to the board game Mastermind. #R##N#We analyze in particular the combined memory-restricted ranking-based black-box complexity of OneMax for different memory sizes. While its isolated memory-restricted as well as its ranking-based black-box complexity for bit strings of length $n$ is only of order $n/\log n$, the combined model does not allow for algorithms being faster than linear in $n$, as can be seen by standard information-theoretic considerations. We show that this linear bound is indeed asymptotically tight. Similar results are obtained for other memory- and offspring-sizes. Our results also apply to the (Monte Carlo) complexity of OneMax in the recently introduced elitist model, in which only the best-so-far solution can be kept in the memory. Finally, we also provide improved lower bounds for the complexity of OneMax in the regarded models. #R##N#Our result enlivens the quest for natural evolutionary algorithms optimizing OneMax in $o(n \log n)$ iterations.

Connected through hyperedge HE-1 (induced by source paper "complexity theory for discrete black box optimization heuristics"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "the 1 1 elitist black box complexity of leadingones"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "playing mastermind with constant size memory"

Connected through hyperedge HE-2 (induced by source paper "the linear hidden subset problem for the 1 1 ea with scheduled and adaptive mutation rates"):
  with members:
    - "bounding bloat in genetic programming"
    - "solving problems with unknown solution length at almost no extra cost"

Connected through hyperedge HE-3 (induced by source paper "the 1 1 elitist black box complexity of leadingones"):
  with members:
    - "ranking based black box complexity"
    - "black box complexity breaking the o n log n barrier of leadingones"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "playing mastermind with constant size memory"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-4 (induced by source paper "update strength in edas and aco how to avoid genetic drift"):
  with members:
    - "a new method for lower bounds on the running time of evolutionary algorithms"

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)