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: "first improvement vs best improvement local optima networks of nk landscapes"
  Abstract: This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepest-ascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.

Connected through hyperedge HE-1 (induced by source paper "a study of fitness landscapes for neuroevolution"):
  with members:
    - "evolving deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "convolution by evolution differentiable pattern producing networks"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-2 (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"
    - "local optima networks and the performance of iterated local search"
    - "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-3 (induced by source paper "local optima networks a new model of combinatorial fitness landscapes"):
  with members:
    - "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 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-4 (induced by source paper "local optima networks and the performance of iterated local search"):
  with members:
    - "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"
    - "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.NE(Neural and Evolutionary Computing)