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 "commutative algorithms approximate the lll distribution"):
  Abstract: Following the groundbreaking Moser-Tardos algorithm for the Lovasz Local Lemma (LLL), a series of works have exploited a key ingredient of the original analysis, the witness tree lemma, in order to: derive deterministic, parallel and distributed algorithms for the LLL, to estimate the entropy of the output distribution, to partially avoid bad events, to deal with super-polynomially many bad events, and even to devise new algorithmic frameworks. Meanwhile, a parallel line of work, has established tools for analyzing stochastic local search algorithms motivated by the LLL that do not fall within the Moser-Tardos framework. Unfortunately, the aforementioned results do not transfer to these more general settings. Mainly, this is because the witness tree lemma, provably, no longer holds. Here we prove that for commutative algorithms, a class recently introduced by Kolmogorov and which captures the vast majority of LLL applications, the witness tree lemma does hold. Armed with this fact, we extend the main result of Haeupler, Saha, and Srinivasan to commutative algorithms, establishing that the output of such algorithms well-approximates the LLL-distribution, i.e., the distribution obtained by conditioning on all bad events being avoided, and give several new applications. For example, we show that the recent algorithm of Molloy for list coloring number of sparse, triangle-free graphs can output exponential many list colorings of the input graph.

Includes members:
  Member "a new perspective on stochastic local search and the lovasz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficiently list edge coloring multigraphs asymptotically optimally")
  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 "deterministic algorithms for the lovasz local lemma simpler more general and more parallel")
  Member "inapproximability of the independent set polynomial below the shearer threshold":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the complexity of approximating the matching polynomial in the complex plane")
  Member "computing the independence polynomial in shearer s region for the lll":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variable version lov asz local lemma beyond shearer s bound")
  Member "algorithmic and enumerative aspects of the moser tardos distribution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (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.DM(Discrete Mathematics)