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 the bi objective black box optimization benchmarking bbob biobj test suite"
  Abstract: The bbob-biobj test suite contains 55 bi-objective functions in continuous domain which are derived from combining functions of the well-known single-objective noiseless bbob test suite. Besides giving the actual function definitions and presenting their (known) properties, this documentation also aims at giving the rationale behind our approach in terms of function groups, instances, and potential objective space normalization.

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 "optimizing deep neural networks with multiple search neuroevolution"):
  with members:
    - "experiential robot learning with accelerated neuroevolution"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "ssd single shot multibox detector"
    - "an overview of gradient descent optimization algorithms"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep spatial autoencoders for visuomotor learning"

Connected through hyperedge HE-3 (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 performance assessment"
    - "evaluating las vegas algorithms pitfalls and remedies"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-4 (induced by source paper "biobjective performance assessment with the coco platform"):
  with members:
    - "coco the experimental procedure"
    - "coco a platform for comparing continuous optimizers in a black box setting"

Connected through hyperedge HE-5 (induced by source paper "surrogate assisted partial order based evolutionary optimisation"):
  with members:
    - "biobjective performance assessment with the coco platform"

Connected through hyperedge HE-6 (induced by source paper "anytime bi objective optimization with a hybrid multi objective cma es hmo cma es"):
  with members:
    - "coco the experimental procedure"

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.AI(Artificial Intelligence)