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: "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
  Abstract: IOHprofiler is a new tool for analyzing and comparing iterative optimization heuristics. Given as input algorithms and problems written in C or Python, it provides as output a statistical evaluation of the algorithms' performance by means of the distribution on the fixed-target running time and the fixed-budget function values. In addition, IOHprofiler also allows to track the evolution of algorithm parameters, making our tool particularly useful for the analysis, comparison, and design of (self-)adaptive algorithms. #R##N#IOHprofiler is a ready-to-use software. It consists of two parts: an experimental part, which generates the running time data, and a post-processing part, which produces the summarizing comparisons and statistical evaluations. The experimental part is build on the COCO software, which has been adjusted to cope with optimization problems that are formulated as functions $f:\mathcal{S}^n \to \R$ with $\mathcal{S}$ being a discrete alphabet of integers. The post-processing part is our own work. It can be used as a stand-alone tool for the evaluation of running time data of arbitrary benchmark problems. It accepts as input files not only the output files of IOHprofiler, but also original COCO data files. The post-processing tool is designed for an interactive evaluation, allowing the user to chose the ranges and the precision of the displayed data according to his/her needs. #R##N#IOHprofiler is available on GitHub at \url{this https URL}.

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 "sequential vs integrated algorithm selection and configuration a case study for the modular cma es"):
  with members:
    - "online selection of cma es variants"
    - "automated algorithm selection survey and perspectives"
    - "pitfalls and best practices in algorithm configuration"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "evolving the structure of evolution strategies"
    - "coco performance assessment"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-3 (induced by source paper "interpolating local and global search by controlling the variance of standard bit mutation"):
  with members:
    - "maximizing drift is not optimal for solving onemax"
    - "optimal parameter choices via precise black box analysis"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems"
    - "fast genetic algorithms"

Connected through hyperedge HE-4 (induced by source paper "hyper parameter tuning for the 1 λ λ ga"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

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)