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 "typically correct derandomization for small time and space"):
  Abstract: Suppose a language $L$ can be decided by a bounded-error randomized algorithm that runs in space $S$ and time $n \cdot \text{poly}(S)$. We give a randomized algorithm for $L$ that still runs in space $O(S)$ and time $n \cdot \text{poly}(S)$ that uses only $O(S)$ random bits; our algorithm has a low failure probability on all but a negligible fraction of inputs of each length. An immediate corollary is a deterministic algorithm for $L$ that runs in space $O(S)$ and succeeds on all but a negligible fraction of inputs of each length. #R##N#Next, suppose a language $L$ can be decided by a nondeterministic algorithm that runs in space $S$ and time $n \cdot \text{poly}(S)$. We give an unambiguous algorithm for $L$ that runs in space $O(S \sqrt{\log S})$ and time $2^{O(S)}$ that succeeds on all but a negligible fraction of inputs of each length. #R##N#Finally, we prove that $\mathbf{BPL} \subseteq \mathbf{L}/(n + O(\log^2 n))$ and $\mathbf{NL} \subseteq \mathbf{UL}/(n + O(\log^2 n))$, improving results by Fortnow and Klivans (STACS '06) and Reinhardt and Allender (SICOMP '00), respectively. If the original randomized/nondeterministic algorithm runs in quasilinear time, we show that fewer than $n$ bits of advice suffice (for disambiguation, this involves increasing the space complexity to $O(\log n \sqrt{\log \log n})$).

Includes members:
  Member "an illuminating algorithm for the light bulb problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a faster algorithm enumerating relevant features over finite fields")
  Member "revisiting norm estimation in data streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal error pseudodistributions for read once branching programs")

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)