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: "destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming"
  Abstract: For theoretical analyses there are two specifics distinguishing GP from many other areas of evolutionary computation. First, the variable size representations, in particular yielding a possible bloat (i.e. the growth of individuals with redundant parts). Second, the role and realization of crossover, which is particularly central in GP due to the tree-based representation. Whereas some theoretical work on GP has studied the effects of bloat, crossover had a surprisingly little share in this work. We analyze a simple crossover operator in combination with local search, where a preference for small solutions minimizes bloat (lexicographic parsimony pressure); the resulting algorithm is denoted Concatenation Crossover GP. For this purpose three variants of the well-studied MAJORITY test function with large plateaus are considered. We show that the Concatenation Crossover GP can efficiently optimize these test functions, while local search cannot be efficient for all three variants independent of employing bloat control.

Connected through hyperedge HE-1 (induced by source paper "computational complexity analysis of genetic programming"):
  with members:
    - "evolving boolean functions with conjunctions and disjunctions via genetic programming"
    - "on inversely proportional hypermutations with mutation potential"
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "bounding bloat in genetic programming"
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "level based analysis of genetic algorithms and other search processes"
    - "computational complexity analysis of multi objective genetic programming"
    - "computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics"

Connected through hyperedge HE-2 (induced by source paper "exponential slowdown for larger populations the µ 1 ea on monotone functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "optimizing monotone functions can be difficult"
    - "on the impact of mutation selection balance on the runtime of evolutionary 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)