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 "derandomizing the lovasz local lemma via log space statistical tests"):
  Abstract: The Lov\'{a}sz Local Lemma (LLL) is a keystone principle in probability theory, guaranteeing the existence of configurations which avoid a collection $\mathcal B$ of "bad" events which are mostly independent and have low probability. In its simplest form, it asserts that whenever a bad-event has probability $p$ and affects at most $d$ other bad-events, and $e p (d+1) < 1$, then a configuration avoiding all $\mathcal B$ exists. #R##N#A seminal algorithm of Moser & Tardos (2010) gives randomized algorithms for most constructions based on the LLL. However, deterministic algorithms have lagged behind. Notably, prior deterministic LLL algorithms have required stringent conditions on $\mathcal B$; for example, they have required that events in $\mathcal B$ have low decision-tree complexity or depend on a small number of variables. For this reason, they can only be applied to small fraction of the numerous LLL applications in practice. #R##N#We describe an alternate deterministic parallel (NC) algorithm for the LLL, based on a general derandomization method of Sivakumar (2002) using log-space statistical tests. The only requirement here is that bad-events should be computable via a finite automaton with $\text{poly}(d)$ states. This covers most LLL applications to graph theory and combinatorics. No auxiliary information about the bad-events, including any conditional probability calculations, are required. Additionally, the proof is a straightforward combination of general derandomization results and high-level analysis of the Moser-Tardos algorithm. #R##N#We illustrate with applications to defective vertex coloring, domatic partition, and independent transversals.

Includes members:
  Member "deterministic parallel algorithms for bilinear objective functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space")
  Member "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "classification of distributed binary labeling problems")
  Member "deterministic parallel algorithms for fooling polylogarithmic juntas and the lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel")
  Member "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast sampling and counting k sat solutions in the local lemma regime")

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.DS(Data Structures and Algorithms)