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: "faster black box algorithms through higher arity operators"
  Abstract: We extend the work of Lehre and Witt (GECCO 2010) on the unbiased black-box model by considering higher arity variation operators. In particular, we show that already for binary operators the black-box complexity of \leadingones drops from $\Theta(n^2)$ for unary operators to $O(n \log n)$. For \onemax, the $\Omega(n \log n)$ unary black-box complexity drops to O(n) in the binary case. For $k$-ary operators, $k \leq n$, the \onemax-complexity further decreases to $O(n/\log k)$.

Connected through hyperedge HE-1 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler"):
  with members:
    - "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates"
    - "interpolating local and global search by controlling the variance of standard bit mutation"
    - "hyper parameter tuning for the 1 λ λ ga"
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "complexity theory for discrete black box optimization heuristics"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"

Connected through hyperedge HE-2 (induced by source paper "black box complexity of the binary value function"):
  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"
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "more effective crossover operators for the all pairs shortest path problem"
    - "playing mastermind with constant size memory"

Connected through hyperedge HE-3 (induced by source paper "better fixed arity unbiased black box algorithms"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "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"
    - "playing mastermind with constant size memory"

Connected through hyperedge HE-4 (induced by source paper "the query complexity of a permutation based variant of mastermind"):
  with members:
    - "complexity theory for discrete black box optimization heuristics"
    - "the 1 1 elitist black box complexity of leadingones"
    - "playing mastermind with many colors"

Connected through hyperedge HE-5 (induced by source paper "the 1 1 elitist black box complexity of leadingones"):
  with members:
    - "onemax in black box models with several restrictions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "onemax in black box models with several restrictions"):
  with members:
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "playing mastermind with constant size memory"

Connected through hyperedge HE-7 (induced by source paper "introducing elitist black box models when does elitist selection weaken the performance of evolutionary algorithms"):
  with members:
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "black box complexities of combinatorial problems"

Connected through hyperedge HE-8 (induced by source paper "unbiased black box complexities of jump functions"):
  with members:
    - "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"
    - "black box complexities of combinatorial problems"
    - "playing mastermind with constant size memory"

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)