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 of nk landscapes with neutrality"
  Abstract: In previous work we have introduced a network-based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here we extend this formalism to neutral fitness landscapes, which are common in difficult combinatorial search spaces. By using two known neutral variants of the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned by a parameter, we show that our new definitions of the optima networks and the associated basins are consistent with the previous definitions for the non-neutral case. Moreover, our empirical study and statistical analysis show that the features of neutral landscapes interpolate smoothly between landscapes with maximum neutrality and non-neutral ones. We found some unknown structural differences between the two studied families of neutral landscapes. But overall, the network features studied confirmed that neutrality, in landscapes with percolating neutral networks, may enhance heuristic search. Our current methodology requires the exhaustive enumeration of the underlying search space. Therefore, sampling techniques should be developed before this analysis can have practical implications. We argue, however, that the proposed model offers a new perspective into the problem difficulty of combinatorial optimization problems and may inspire the design of more effective search heuristics.

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 and the performance of iterated local search"
    - "local optima networks landscape autocorrelation and heuristic search performance"
    - "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 and the performance of iterated local search"
    - "local optima networks landscape autocorrelation and heuristic search performance"
    - "communities of minima in local optima networks of combinatorial spaces"
    - "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-3 (induced by source paper "simulating non stationary operators in search algorithms"):
  with members:
    - "an experimental study of adaptive control for evolutionary algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "local optima networks and the performance of iterated local search"):
  with members:
    - "first improvement vs best improvement local optima networks of nk landscapes"
    - "communities of minima in local optima networks of combinatorial spaces"
    - "local optima networks of the quadratic assignment problem"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-5 (induced by source paper "local optima networks landscape autocorrelation and heuristic search performance"):
  with members:
    - "communities of minima in local optima networks of combinatorial spaces"
    - "local optima networks of the quadratic assignment problem"
    - "elementary landscape decomposition of the quadratic assignment problem"

Connected through hyperedge HE-6 (induced by source paper "first improvement vs best improvement local optima networks of nk landscapes"):
  with members:
    - "local optima networks of the quadratic assignment problem"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-7 (induced by source paper "communities of minima in local optima networks of combinatorial spaces"):
  with members:
    - "local optima networks of the quadratic assignment problem"

Connected through hyperedge HE-8 (induced by source paper "clustering of local optima in combinatorial fitness landscapes"):
  with members:
    - "local optima networks of the quadratic assignment problem"

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)