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: "the connectivity of nk landscapes basins a network analysis"
  Abstract: We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces. We use the well-known family of NK landscapes as an example. In our case the inherent network is the graph where the vertices represent the local maxima in the landscape, and the edges account for the transition probabilities between their corresponding basins of attraction. We exhaustively extracted such networks on representative small NK landscape instances, and performed a statistical characterization of their properties. We found that most of these network properties can be related to the search difficulty on the underlying NK landscapes with varying values of K.

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 nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "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 nk landscapes with neutrality"
    - "local optima networks of the quadratic assignment problem"
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-3 (induced by source paper "local optima networks of nk landscapes with neutrality"):
  with members:
    - "a study of nk landscapes basins and local optima networks"
    - "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-4 (induced by source paper "nk landscapes difficulty and negative slope coefficient how sampling influences the results"):
  with members:
    - "a study of nk landscapes basins and local optima networks"
    - "anisotropic selection in cellular genetic algorithms"

Connected through hyperedge HE-5 (induced by source paper "local optima networks of the quadratic assignment problem"):
  with members:
    - "a study of nk landscapes basins and local optima networks"

Connected through hyperedge HE-6 (induced by source paper "centric selection a way to tune the exploration exploitation trade off"):
  with members:
    - "a study of nk landscapes basins and local optima networks"
    - "anisotropic selection in cellular genetic algorithms"

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)