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: "constructive derandomization of query algorithms"
  Abstract: We give efficient deterministic algorithms for converting randomized query algorithms into deterministic ones. We first give an algorithm that takes as input a randomized $q$-query algorithm $R$ with description length $N$ and a parameter $\varepsilon$, runs in time $\mathrm{poly}(N) \cdot 2^{O(q/\varepsilon)}$, and returns a deterministic $O(q/\varepsilon)$-query algorithm $D$ that $\varepsilon$-approximates the acceptance probabilities of $R$. These parameters are near-optimal: runtime $N + 2^{\Omega(q/\varepsilon)}$ and query complexity $\Omega(q/\varepsilon)$ are necessary. #R##N#Next, we give algorithms for instance-optimal and online versions of the problem: #R##N#$\circ$ Instance optimal: Construct a deterministic $q^\star_R$-query algorithm $D$, where $q^\star_R$ is minimum query complexity of any deterministic algorithm that $\varepsilon$-approximates $R$. #R##N#$\circ$ Online: Deterministically approximate the acceptance probability of $R$ for a specific input $\underline{x}$ in time $\mathrm{poly}(N,q,1/\varepsilon)$, without constructing $D$ in its entirety. #R##N#Applying the techniques we develop for these extensions, we constructivize classic results that relate the deterministic, randomized, and quantum query complexities of boolean functions (Nisan, STOC 1989; Beals et al., FOCS 1998). This has direct implications for the Turing machine model of computation: sublinear-time algorithms for total decision problems can be efficiently derandomized and dequantized with a subexponential-time preprocessing step.

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.CC(Computational Complexity)