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: "an algorithmic proof of the lovasz local lemma via resampling oracles"
  Abstract: The Lovasz Local Lemma is a seminal result in probabilistic combinatorics. It gives a sufficient condition on a probability space and a collection of events for the existence of an outcome that simultaneously avoids all of those events. Finding such an outcome by an efficient algorithm has been an active research topic for decades. Breakthrough work of Moser and Tardos (2009) presented an efficient algorithm for a general setting primarily characterized by a product structure on the probability space. #R##N#In this work we present an efficient algorithm for a much more general setting. Our main assumption is that there exist certain functions, called resampling oracles, that can be invoked to address the undesired occurrence of the events. We show that, in all scenarios to which the original Lovasz Local Lemma applies, there exist resampling oracles, although they are not necessarily efficient. Nevertheless, for essentially all known applications of the Lovasz Local Lemma and its generalizations, we have designed efficient resampling oracles. As applications of these techniques, we present new results for packings of Latin transversals, rainbow matchings and rainbow spanning trees.

Connected through hyperedge HE-1 (induced by source paper "extensions of the algorithmic lovasz local lemma"):
  with members:
    - "beyond the lovasz local lemma point to set correlations and their algorithmic applications"
    - "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"
    - "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 "dynamic sampling from graphical models"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "tight bounds for popping algorithms"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "what can be sampled locally"
    - "a polynomial time approximation algorithm for all terminal network reliability"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"

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"
    - "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"
    - "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 "efficiently list edge coloring multigraphs asymptotically optimally"):
  with members:
    - "a new perspective on stochastic local search and the lovasz local lemma"
    - "a local lemma for focused stochastic algorithms"
    - "commutative algorithms approximate the lll distribution"
    - "stochastic control via entropy compression"
    - "commutativity in the algorithmic lovasz local lemma"
    - "entropy optimization and counting"

Connected through hyperedge HE-5 (induced by source paper "local computation algorithms for the lovasz local lemma"):
  with members:
    - "a new perspective on stochastic local search and the lovasz local lemma"
    - "on the complexity of local distributed graph problems"
    - "commutativity in the algorithmic lovasz local lemma"
    - "local computation mechanism design"

Connected through hyperedge HE-6 (induced by source paper "quantum lov a sz local lemma shearer s bound is tight"):
  with members:
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "computing the independence polynomial from the tree threshold down to the roots"
    - "a constructive proof of the general lovasz local lemma"

Connected through hyperedge HE-7 (induced by source paper "a local lemma for focused stochastic algorithms"):
  with members:
    - "commutativity in the algorithmic lovasz local lemma"

Connected through hyperedge HE-8 (induced by source paper "a time hierarchy theorem for the local model"):
  with members:
    - "the complexity of distributed edge coloring with small palettes"
    - "lcl problems on grids"
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "polynomial lower bound for distributed graph coloring in a weak local model"
    - "distributed degree splitting edge coloring and orientations"
    - "survey of distributed decision"
    - "an exponential separation between randomized and deterministic complexity in the local model"
    - "on the complexity of local distributed graph problems"

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.DS(Data Structures and Algorithms)