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: "memetic artificial bee colony algorithm for large scale global optimization"
  Abstract: Memetic computation (MC) has emerged recently as a new paradigm of efficient algorithms for solving the hardest optimization problems. On the other hand, artificial bees colony (ABC) algorithms demonstrate good performances when solving continuous and combinatorial optimization problems. This study tries to use these technologies under the same roof. As a result, a memetic ABC (MABC) algorithm has been developed that is hybridized with two local search heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction exploitation (RWDE). The former is attended more towards exploration, while the latter more towards exploitation of the search space. The stochastic adaptation rule was employed in order to control the balancing between exploration and exploitation. This MABC algorithm was applied to a Special suite on Large Scale Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary Computation. The obtained results the MABC are comparable with the results of DECC-G, DECC-G*, and MLCC.

Connected through hyperedge HE-1 (induced by source paper "improved onlooker bee phase in artificial bee colony algorithm"):
  with members:
    - "randomized memetic artificial bee colony algorithm"
    - "a novel hybrid crossover based artificial bee colony algorithm for optimization problem"

Connected through hyperedge HE-2 (induced by source paper "randomized memetic artificial bee colony algorithm"):
  with members:
    - "a novel hybrid crossover based artificial bee colony algorithm for optimization problem"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive review of firefly algorithms"):
  with members:
    - "graph 3 coloring with a hybrid self adaptive evolutionary algorithm"
    - "analysis of a nature inspired firefly algorithm based back propagation neural network training"
    - "a novel rough set reduct algorithm for medical domain based on bee colony optimization"
    - "distributed graph coloring an approach based on the calling behavior of japanese tree frogs"

Connected through hyperedge HE-4 (induced by source paper "a hybrid bat algorithm"):
  with members:
    - "graph 3 coloring with a hybrid self adaptive evolutionary algorithm"
    - "a hybrid artificial bee colony algorithm for graph 3 coloring"

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)