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: "coco performance assessment"
  Abstract: We present an any-time performance assessment for benchmarking numerical optimization algorithms in a black-box scenario, applied within the COCO benchmarking platform. The performance assessment is based on runtimes measured in number of objective function evaluations to reach one or several quality indicator target values. We argue that runtime is the only available measure with a generic, meaningful, and quantitative interpretation. We discuss the choice of the target values, runlength-based targets, and the aggregation of results by using simulated restarts, averages, and empirical distribution functions.

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"
    - "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
    - "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"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-3 (induced by source paper "meta surrogate benchmarking for hyperparameter optimization"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "tabular benchmarks for joint architecture and hyperparameter optimization"
    - "bayesian optimization in alphago"
    - "on the state of the art of evaluation in neural language models"
    - "population based training of neural networks"
    - "xgboost a scalable tree boosting system"
    - "coco a platform for comparing continuous optimizers in a black box setting"
    - "non stochastic best arm identification and hyperparameter optimization"

Connected through hyperedge HE-4 (induced by source paper "online selection of cma es variants"):
  with members:
    - "automated algorithm selection survey and perspectives"
    - "evolving the structure of evolution strategies"
    - "cma es with two point step size adaptation"

Connected through hyperedge HE-5 (induced by source paper "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "coco a platform for comparing continuous optimizers in a black box setting"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"

Connected through hyperedge HE-6 (induced by source paper "coco a platform for comparing continuous optimizers in a black box setting"):
  with members:
    - "biobjective performance assessment with the coco platform"
    - "coco the experimental procedure"
    - "coco the bi objective black box optimization benchmarking bbob biobj test suite"
    - "evaluating las vegas algorithms pitfalls and remedies"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-7 (induced by source paper "coco the experimental procedure"):
  with members:
    - "coco a platform for comparing continuous optimizers in a black box setting"
    - "evaluating las vegas algorithms pitfalls and remedies"

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)