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 "explaining optimization in genetic algorithms with uniform crossover"):
  Abstract: Hyperclimbing is an intuitive, general-purpose, global optimization heuristic applicable to discrete product spaces with rugged or stochastic cost functions. The strength of this heuristic lies in its insusceptibility to local optima when the cost function is deterministic, and its tolerance for noise when the cost function is stochastic. Hyperclimbing works by decimating a search space, i.e., by iteratively fixing the values of small numbers of variables. The hyperclimbing hypothesis posits that genetic algorithms with uniform crossover (UGAs) perform optimization by implementing efficient hyperclimbing. Proof of concept for the hyperclimbing hypothesis comes from the use of an analytic technique that exploits algorithmic symmetry. By way of validation, we present experimental results showing that a simple tweak inspired by the hyperclimbing hypothesis dramatically improves the performance of a UGA on large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin Glasses problem. An exciting corollary of the hyperclimbing hypothesis is that a form of implicit parallelism more powerful than the kind described by Holland underlies optimization in UGAs. The implications of the hyperclimbing hypothesis for Evolutionary Computation and Artificial Intelligence are discussed.

Includes members:
  Member "sufficient conditions for coarse graining evolutionary dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the fundamental learning problem that genetic algorithms with uniform crossover solve efficiently and repeatedly as evolution proceeds")
  Member "survey propagation an algorithm for satisfiability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers")

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)