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 "deterministic algorithms for the lovasz local lemma simpler more general and more parallel"):
  Abstract: The Lovasz 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 "symmetric" form, it asserts that whenever a bad-event has probability $p$ and affects at most $d$ bad-events, and $e p d < 1$, then a configuration avoiding all $\mathcal B$ exists. #R##N#A seminal algorithm of Moser & Tardos (2010) gives nearly-automatic randomized algorithms for most constructions based on the LLL. However, deterministic algorithms have lagged behind. We address three specific shortcomings of the prior deterministic algorithms. First, our algorithm applies to the LLL criterion of Shearer (1985); this is more powerful than alternate LLL criteria and also removes a number of nuisance parameters and leads to cleaner and more legible bounds. Second, we provide parallel algorithms with much greater flexibility in the functional form of of the bad-events. Third, we provide a derandomized version of the MT-distribution, that is, the distribution of the variables at the termination of the MT algorithm. #R##N#We show applications to non-repetitive vertex coloring, independent transversals, strong coloring, and other problems. These give deterministic algorithms which essentially match the best previous randomized sequential and parallel algorithms.

Includes members:
  Member "algorithms for weighted independent transversals and strong colouring":
    also connected through other hyperedges:
      (none)
  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 "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extensions of the algorithmic lovasz local lemma")
  Member "deterministic parallel algorithms for fooling polylogarithmic juntas and the lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "derandomizing the lovasz local lemma via log space statistical tests")
  Member "algorithmic and enumerative aspects of the moser tardos distribution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms for weighted independent transversals and strong colouring")
  Member "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast sampling and counting k sat solutions in the local lemma regime")
  Member "new constructive aspects of the lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)