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 "parallel algorithms for the lopsided lovasz local lemma"):
  Abstract: The Lov\'asz Local Lemma (LLL) is a probabilistic tool which shows that, if a collection of "bad" events $\mathcal B$ in a probability space are not too likely and not too interdependent, then there is a positive probability that no bad-events in $\mathcal B$ occur. Moser & Tardos (2010) gave sequential and parallel algorithms which transformed most applications of the variable-assignment LLL into efficient algorithms. #R##N#There has been limited success in developing parallel algorithms for more generalized forms of the LLL. Harris (2016) developed RNC algorithms for the variable-assignment Lopsided Lov\'asz Local Lemma (LLLL) and Harris & Srinivasan (2014) developed an algorithm for the permutation LLL. These algorithms are cumbersome and limited in the types of bad-events they can handle (limitations not shared for the standard LLL). Kolmogorov (2016) developed a framework which partially parallelizes LLL settings such as random matchings of $K_n$, although key algorithm components remained missing. #R##N#We give new parallel algorithms for most forms of LLLL, which are simpler, faster, and more general than the algorithms of Harris and Harris & Srinivasan. This also includes probability spaces for which no previous RNC algorithm was known, including matchings and hamiltonian cycles of $K_n$. We achieve this by providing a unified algebraic framework for applications of the LLLL to permutations. #R##N#The parallel LLLL algorithm is based on new primitive for parallel computing, which we refer to as the \emph{lexicographically-first maximal-independent-set} of a directed graph. We give an efficient algorithm for constructing this and show that it is precisely what is needed for our LLLL algorithms. This generalizes an algorithm given by Blelloch, Fineman, Shun (2012) for undirected graphs.

Includes members:
  Member "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel")
  Member "algorithmic and enumerative aspects of the moser tardos distribution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel")
  Member "commutativity in the algorithmic lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extensions of the algorithmic lovasz local lemma")
  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")
  Member "an improved distributed algorithm for maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "greedy sequential maximal independent set and matching are parallel on average":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relaxed scheduling for scalable belief propagation")

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)