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 "working principles of binary differential evolution"):
  Abstract: Abstract   We conduct a first fundamental analysis of the working principles of binary differential evolution (BDE), an optimization heuristic for binary decision variables that was derived by Gong and Tuson (2007) from the very successful classic differential evolution (DE) for continuous optimization. We show that unlike most other optimization paradigms, it is stable in the sense that neutral bit values are sampled with probability close to 1/2 for a long time. This is generally a desirable property, however, it makes it harder to find the optima for decision variables with small influence on the objective function. This can result in an optimization time exponential in the dimension when optimizing simple symmetric functions like OneMax. On the positive side, BDE quickly detects and optimizes the most important decision variables. For example, dominant bits converge to the optimal value in time logarithmic in the population size. This enables BDE to optimize the most important bits very fast. Overall, our results indicate that BDE is an interesting optimization paradigm having characteristics significantly different from classic evolutionary algorithms or estimation-of-distribution algorithms (EDAs).  On the technical side, we observe that the strong stochastic dependencies in the random experiment describing a run of BDE prevent us from proving all desired results with the mathematical rigor that was successfully used in the analysis of other evolutionary algorithms. Inspired by mean-field approaches in statistical physics we propose a more independent variant of BDE, show experimentally its similarity to BDE, and prove some statements rigorously only for the independent variant. Such a semi-rigorous approach might be interesting for other problems in evolutionary computation where purely mathematical methods failed so far.

Includes members:
  Member "probabilistic tools for the analysis of randomized optimization heuristics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling")
  Member "fast re optimization via structural diversity":
    also connected through other hyperedges:
      (none)
  Member "upper bounds on the running time of the univariate marginal distribution algorithm on onemax":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sharp bounds for genetic drift in edas")
  Member "better runtime guarantees via stochastic domination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "significance based estimation of distribution algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help")
  Member "level based analysis of the population based incremental learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help")
  Member "the query complexity of a permutation based variant of mastermind":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler")
  Member "update strength in edas and aco how to avoid genetic drift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")

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)