human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "evolving boolean functions with conjunctions and disjunctions via genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computational complexity analysis of genetic programming")
  Member "bounding bloat in genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computational complexity analysis of genetic programming")
  Member "optimal epidemic dissemination":
    also connected through other hyperedges:
      (none)
  Member "adaptive drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "computational complexity analysis of multi objective genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computational complexity analysis of genetic programming")
  Member "computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational complexity analysis of genetic programming")

Question: Please tell me which class the source paper 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)