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: "general drift analysis with tail bounds"
  Abstract: Drift analysis is one of the state-of-the-art techniques for the runtime analysis of randomized search heuristics (RSHs) such as evolutionary algorithms (EAs), simulated annealing etc. The vast majority of existing drift theorems yield bounds on the expected value of the hitting time for a target state, e.g., the set of optimal solutions, without making additional statements on the distribution of this time. We address this lack by providing a general drift theorem that includes bounds on the upper and lower tail of the hitting time distribution. The new tail bounds are applied to prove very precise sharp-concentration results on the running time of a simple EA on standard benchmark problems, including the class of general linear functions. Surprisingly, the probability of deviating by an $r$-factor in lower order terms of the expected time decreases exponentially with $r$ on all these problems. The usefulness of the theorem outside the theory of RSHs is demonstrated by deriving tail bounds on the number of cycles in random permutations. All these results handle a position-dependent (variable) drift that was not covered by previous drift theorems with tail bounds. Moreover, our theorem can be specialized into virtually all existing drift theorems with drift towards the target from the literature. Finally, user-friendly specializations of the general drift theorem are given.

Connected through hyperedge HE-1 (induced by source paper "sharp bounds on the runtime of the 1 1 ea via drift analysis and analytic combinatorial tools"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "fitness probability distribution of bit flip mutation"
    - "multiplicative drift analysis"

Connected through hyperedge HE-2 (induced by source paper "theory of estimation of distribution algorithms"):
  with members:
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "update strength in edas and aco how to avoid genetic drift"
    - "level based analysis of genetic algorithms and other search processes"
    - "multiplicative drift analysis"

Connected through hyperedge HE-3 (induced by source paper "analysing the robustness of evolutionary algorithms to noise refined runtime bounds and an example where noise is beneficial"):
  with members:
    - "analysis of noisy evolutionary optimization when sampling fails"
    - "memetic algorithms beat evolutionary algorithms on the class of hurdle problems"
    - "analyzing evolutionary optimization in noisy environments"
    - "erratum simplified drift analysis for proving lower bounds in evolutionary computation"

Connected through hyperedge HE-4 (induced by source paper "drift theory in continuous search spaces expected hitting time of the 1 1 es with 1 5 success rule"):
  with members:
    - "linear convergence on positively homogeneous functions of a comparison based step size adaptive randomized search the 1 1 es with generalized one fifth success rule"
    - "multiplicative drift analysis"

Connected through hyperedge HE-5 (induced by source paper "global convergence of the 1 1 evolution strategy"):
  with members:
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-6 (induced by source paper "optimal dynamic strings"):
  with members:
    - "dynamic index lz factorization and lce queries in compressed space"
    - "recompression a simple and powerful technique for word equations"
    - "faster fully compressed pattern matching by recompression"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-7 (induced by source paper "on the diameter of hyperbolic random graphs"):
  with members:
    - "network mapping by replaying hyperbolic growth"
    - "geometric inhomogeneous random graphs"
    - "fast generation of dynamic complex networks with underlying hyperbolic geometry"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-8 (induced by source paper "the fitness level method with tail bounds"):
  with members:
    - "a new method for lower bounds on the running time 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)