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: "local optima networks and the performance of iterated local search"
  Abstract: Local Optima Networks (LONs) have been recently proposed as an alternative model of combinatorial fitness landscapes. The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. A new set of metrics can be derived from this model that capture the distribution and connectivity of the local optima in the underlying configuration space. This paper departs from the descriptive analysis of local optima networks, and actively studies the correlation between network features and the performance of a local search heuristic. The NK family of landscapes and the Iterated Local Search metaheuristic are considered. With a statistically-sound approach based on multiple linear regression, it is shown that some LONs' features strongly influence and can even partly predict the performance of a heuristic search algorithm. This study validates the expressive power of LONs as a model of combinatorial fitness landscapes.

Connected through hyperedge HE-1 (induced by source paper "a characterisation of s box fitness landscapes in cryptography"):
  with members:
    - "on the performance of multi objective estimation of distribution algorithms for combinatorial problems"
    - "local optima networks a new model of combinatorial fitness landscapes"
    - "first improvement vs best improvement local optima networks of nk landscapes"
    - "local optima networks landscape autocorrelation and heuristic search performance"
    - "local optima networks of nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "the connectivity of nk landscapes basins a network analysis"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-2 (induced by source paper "local optima networks a new model of combinatorial fitness landscapes"):
  with members:
    - "first improvement vs best improvement local optima networks of nk landscapes"
    - "local optima networks landscape autocorrelation and heuristic search performance"
    - "communities of minima in local optima networks of combinatorial spaces"
    - "local optima networks of nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "the connectivity of nk landscapes basins a network analysis"
    - "a study of nk landscapes basins and local optima networks"

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)