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 "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.

Includes members:
  Member "lifting with simple gadgets and applications to circuit and proof complexity":
    also connected through other hyperedges:
      (none)
  Member "beyond worst case analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer")
  Member "query to communication lifting for bpp":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "the influence lower bound via query elimination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of boolean functions")
  Member "improved direct product theorems for randomized query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a tight composition theorem for the randomized query complexity of partial functions")
  Member "optimal direct sum results for deterministic and randomized decision tree complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a tight composition theorem for the randomized query complexity of partial functions")
  Member "every decision tree has an influential variable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on higher order cryptography long version")
  Member "optimal aggregation algorithms for middleware":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding interesting frames in deep video analytics a top k approach")

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.CC(Computational Complexity)