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: "a literature survey of benchmark functions for global optimisation problems"
  Abstract: Test functions are important to validate and compare the performance of optimisation algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties to be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimisation problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and it can be expected that this complete set of functions can be used for validation of new optimisation in the future.

Connected through hyperedge HE-1 (induced by source paper "nature inspired optimization algorithms challenges and open problems"):
  with members:
    - "self adaptive decision making mechanisms to balance the execution of multiple tasks for a multi robots team"
    - "swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks"
    - "new directional bat algorithm for continuous optimization problems"
    - "a discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy"
    - "an improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems"
    - "a comprehensive review of firefly algorithms"
    - "multi objective flower algorithm for optimization"
    - "bat algorithm literature review and applications"

Connected through hyperedge HE-2 (induced by source paper "global optimization via inverse distance weighting and radial basis functions"):
  with members:
    - "global optimization via inverse distance weighting"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "softadapt techniques for adaptive loss weighting of neural networks with multi part loss functions"):
  with members:
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "autoloss learning discrete schedules for alternate optimization"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "a general and adaptive robust loss function"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "yolo9000 better faster stronger"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-4 (induced by source paper "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "a multi hidden recurrent neural network with a modified grey wolf optimizer"
    - "adaptive evolutionary clustering"

Connected through hyperedge HE-5 (induced by source paper "cost aware multi objective bayesian optimisation"):
  with members:
    - "bayesian optimization with directionally constrained search"
    - "scalarizing functions in bayesian multiobjective optimization"
    - "adam a method for stochastic optimization"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "graduated optimization of black box functions"):
  with members:
    - "scale free online learning"
    - "scalable gradient based tuning of continuous regularization hyperparameters"
    - "on graduated optimization for stochastic non convex problems"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"

Connected through hyperedge HE-7 (induced by source paper "active preference learning based on radial basis functions"):
  with members:
    - "global optimization via inverse distance weighting"
    - "multi objective bayesian optimisation with preferences over objectives"
    - "adam a method for stochastic optimization"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "convergence analysis of beetle antennae search algorithm and its applications"):
  with members:
    - "bas beetle antennae search algorithm for optimization problems"

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)