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: "reducing the arity in unbiased black box complexity"
  Abstract: We show that for all $1<k \leq \log n$ the $k$-ary unbiased black-box complexity of the $n$-dimensional $\onemax$ function class is $O(n/k)$. This indicates that the power of higher arity operators is much stronger than what the previous $O(n/\log k)$ bound by Doerr et al. (Faster black-box algorithms through higher arity operators, Proc. of FOGA 2011, pp. 163--172, ACM, 2011) suggests. #R##N#The key to this result is an encoding strategy, which might be of independent interest. We show that, using $k$-ary unbiased variation operators only, we may simulate an unrestricted memory of size $O(2^k)$ bits.

Connected through hyperedge HE-1 (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"
    - "more effective crossover operators for the all pairs shortest path problem"
    - "playing mastermind with constant size memory"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-2 (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"
    - "onemax in black box models with several restrictions"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "ranking based 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-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"
    - "playing mastermind with constant size memory"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-4 (induced by source paper "theoretical analysis of stochastic search algorithms"):
  with members:
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "level based analysis of genetic algorithms and other search processes"
    - "erratum simplified drift analysis for proving lower bounds in evolutionary computation"

Connected through hyperedge HE-5 (induced by source paper "onemax in black box models with several restrictions"):
  with members:
    - "ranking based black box complexity"
    - "playing mastermind with constant size memory"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-6 (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"
    - "black box complexities of combinatorial problems"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-7 (induced by source paper "unbiased black box complexities of jump functions"):
  with members:
    - "ranking based 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"
    - "faster black box algorithms through higher arity operators"

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)