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 study of nk landscapes basins and local optima networks"
  Abstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces (Doye, 2002). We use the well-known family of $NK$ landscapes as an example. In our case the inherent network is the graph where the vertices are all the local maxima and edges mean basin adjacency between two maxima. We exhaustively extract such networks on representative small NK landscape instances, and show that they are 'small-worlds'. However, the maxima graphs are not random, since their clustering coefficients are much larger than those of corresponding random graphs. Furthermore, the degree distributions are close to exponential instead of Poissonian. We also describe the nature of the basins of attraction and their relationship with the local maxima network.

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"
    - "first improvement vs best improvement local optima networks of nk landscapes"

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"
    - "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 nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "the connectivity of nk landscapes basins a network analysis"

Connected through hyperedge HE-3 (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 nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "the connectivity of nk landscapes basins a network analysis"

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 nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"

Connected through hyperedge HE-5 (induced by source paper "first improvement vs best improvement local optima networks of nk landscapes"):
  with members:
    - "local optima networks of nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"

Connected through hyperedge HE-6 (induced by source paper "local optima networks of nk landscapes with neutrality"):
  with members:
    - "the connectivity of nk landscapes basins a network analysis"
    - "fitness landscape of the cellular automata majority problem view from the olympus"
    - "scuba search when selection meets innovation"
    - "when gravity fails local search topology"

Connected through hyperedge HE-7 (induced by source paper "nk landscapes difficulty and negative slope coefficient how sampling influences the results"):
  with members:
    - "the connectivity of nk landscapes basins a network analysis"
    - "anisotropic selection in cellular genetic algorithms"

Connected through hyperedge HE-8 (induced by source paper "local optima networks of the quadratic assignment problem"):
  with members:
    - "the connectivity of nk landscapes basins a network analysis"

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)