human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "algorithmic and enumerative aspects of the moser tardos distribution"
  Abstract: Moser & Tardos have developed a powerful algorithmic approach (henceforth "MT") to the Lovasz Local Lemma (LLL); the basic operation done in MT and its variants is a search for "bad" events in a current configuration. In the initial stage of MT, the variables are set independently. We examine the distributions on these variables which arise during intermediate stages of MT. We show that these configurations have a more or less "random" form, building further on the "MT-distribution" concept of Haeupler et al. in understanding the (intermediate and) output distribution of MT. This has a variety of algorithmic applications; the most important is that bad events can be found relatively quickly, improving upon MT across the complexity spectrum: it makes some polynomial-time algorithms sub-linear (e.g., for Latin transversals, which are of basic combinatorial interest), gives lower-degree polynomial run-times in some settings, transforms certain super-polynomial-time algorithms into polynomial-time ones, and leads to Las Vegas algorithms for some coloring problems for which only Monte Carlo algorithms were known. #R##N#We show that in certain conditions when the LLL condition is violated, a variant of the MT algorithm can still produce a distribution which avoids most of the bad events. We show in some cases this MT variant can run faster than the original MT algorithm itself, and develop the first-known criterion for the case of the asymmetric LLL. This can be used to find partial Latin transversals -- improving upon earlier bounds of Stein (1975) -- among other applications. We furthermore give applications in enumeration, showing that most applications (where we aim for all or most of the bad events to be avoided) have many more solutions than known before by proving that the MT-distribution has "large" min-entropy and hence that its support-size is large.

Connected through hyperedge HE-1 (induced by source paper "deterministic algorithms for the lovasz local lemma simpler more general and more parallel"):
  with members:
    - "algorithms for weighted independent transversals and strong colouring"
    - "deterministic parallel algorithms for bilinear objective functions"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "deterministic parallel algorithms for fooling polylogarithmic juntas and the lovasz local lemma"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "new constructive aspects of the lovasz local lemma"
    - "deterministic algorithms for the lovasz local lemma"

Connected through hyperedge HE-2 (induced by source paper "algorithms for weighted independent transversals and strong colouring"):
  with members:
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "finding perfect matchings in bipartite hypergraphs"
    - "new constructive aspects of the lovasz local lemma"
    - "deterministic algorithms for the lovasz local lemma"
    - "a constructive proof of the general lovasz local lemma"

Connected through hyperedge HE-3 (induced by source paper "beyond the lovasz local lemma point to set correlations and their algorithmic applications"):
  with members:
    - "a local lemma for focused stochastic algorithms"
    - "commutative algorithms approximate the lll distribution"
    - "commutativity in the algorithmic lovasz local lemma"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "entropy compression method applied to graph colorings"
    - "application of entropy compression in pattern avoidance"
    - "new constructive aspects of the lovasz local lemma"

Connected through hyperedge HE-4 (induced by source paper "commutative algorithms approximate the lll distribution"):
  with members:
    - "a new perspective on stochastic local search and the lovasz local lemma"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "inapproximability of the independent set polynomial below the shearer threshold"
    - "computing the independence polynomial in shearer s region for the lll"
    - "commutativity in the algorithmic lovasz local lemma"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "partial resampling to approximate covering integer programs"

Connected through hyperedge HE-5 (induced by source paper "oblivious resampling oracles and parallel algorithms for the lopsided lovasz local lemma"):
  with members:
    - "beyond the lovasz local lemma point to set correlations and their algorithmic applications"
    - "tight analysis of randomized greedy mis"
    - "on derandomizing local distributed algorithms"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "commutativity in the algorithmic lovasz local lemma"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "an improved distributed algorithm for maximal independent set"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-6 (induced by source paper "the algorithmic lll and the witness tree lemma"):
  with members:
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "inapproximability of the independent set polynomial below the shearer threshold"
    - "computing the independence polynomial in shearer s region for the lll"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "partial resampling to approximate covering integer programs"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"

Connected through hyperedge HE-7 (induced by source paper "parallel algorithms for the lopsided lovasz local lemma"):
  with members:
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "commutativity in the algorithmic lovasz local lemma"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "an improved distributed algorithm for maximal independent set"
    - "greedy sequential maximal independent set and matching are parallel on average"

Question: Please tell me which class the center node 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)