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: "scuba search when selection meets innovation"
  Abstract: We proposed a search heuristic using the scuba diving metaphor. This approach is based on the concept of evolvability and tends to exploit neutrality in fitness landscape. Despite the fact that natural evolution does not directly select for evolvability, the basic idea behind the scuba search heuristic is to explicitly push evolvability to increases. Globally the search process switches between two phases: conquest-of-the-waters and invasion-of-the-land. A comparative study of the algorithm and standard local search heuristics on the NKq-landscapes has shown advantage and limit of the scuba search. To enlighten qualitative differences between neutral search processes, the space is transformed into a connected graph to visualize the pathways that the search is likely to follow.

Connected through hyperedge HE-1 (induced by source paper "nils a neutrality based iterated local search and its application to flowshop scheduling"):
  with members:
    - "on the neutrality of flowshop scheduling fitness landscapes"
    - "fitness landscape of the cellular automata majority problem view from the olympus"

Connected through hyperedge HE-2 (induced by source paper "local optima networks of nk landscapes with neutrality"):
  with members:
    - "the connectivity of nk landscapes basins a network analysis"
    - "a study of nk landscapes basins and local optima networks"
    - "fitness landscape of the cellular automata majority problem view from the olympus"
    - "when gravity fails local search topology"

Connected through hyperedge HE-3 (induced by source paper "on the neutrality of flowshop scheduling fitness landscapes"):
  with members:
    - "nils a neutrality based iterated local search and its application to flowshop scheduling"
    - "fitness landscape of the cellular automata majority problem view from the olympus"
    - "measuring the evolvability landscape to study neutrality"

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)