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: "beyond the lovasz local lemma point to set correlations and their algorithmic applications"
  Abstract: Following the groundbreaking algorithm of Moser and Tardos for the Lovasz Local Lemma (LLL), there has been a plethora of results analyzing local search algorithms for various constraint satisfaction problems. The algorithms considered fall into two broad categories: resampling algorithms, analyzed via different algorithmic LLL conditions; and backtracking algorithms, analyzed via entropy compression arguments. This paper introduces a new convergence condition that seamlessly handles resampling, backtracking, and hybrid algorithms, i.e., algorithms that perform both resampling and backtracking steps. Unlike all past LLL work, our condition replaces the notion of a dependency or causality graph by quantifying point-to-set correlations between bad events. As a result, our condition simultaneously: (i)~captures the most general algorithmic LLL condition known as a special case; (ii)~significantly simplifies the analysis of entropy compression applications; (iii)~relates backtracking algorithms, which are conceptually very different from resampling algorithms, to the LLL; and most importantly (iv)~allows for the analysis of hybrid algorithms, which were outside the scope of previous techniques. We give several applications of our condition, including a new hybrid vertex coloring algorithm that extends the recent breakthrough result of Molloy for coloring triangle-free graphs to arbitrary graphs.

Connected through hyperedge HE-1 (induced by source paper "extensions of the algorithmic lovasz local lemma"):
  with members:
    - "a local lemma for focused stochastic algorithms"
    - "commutative algorithms approximate the lll distribution"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "commutativity in the algorithmic lovasz local lemma"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "deterministic algorithms for the lovasz local lemma"
    - "a constructive proof of the general lovasz local lemma"

Connected through hyperedge HE-2 (induced by source paper "oblivious resampling oracles and parallel algorithms for the lopsided lovasz local lemma"):
  with members:
    - "tight analysis of randomized greedy mis"
    - "on derandomizing local distributed algorithms"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "algorithmic and enumerative aspects of the moser tardos distribution"
    - "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)