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: "commutativity in the algorithmic lovasz local lemma"
  Abstract: We consider the recent formulation of the Algorithmic Lov\'asz Local Lemma [9,2] for finding objects that avoid "bad features", or "flaws". It extends the Moser-Tardos resampling algorithm [15] to more general discrete spaces. At each step the method picks a flaw present in the current state and "resamples" it using a "resampling oracle" provided by the user. However, it is less flexible than the Moser-Tardos method since [9,2] require a specific flaw selection rule, whereas [15] allows an arbitrary rule (and thus can potentially be implemented more efficiently). #R##N#We formulate a new "commutativity" condition, and prove that it is sufficient for an arbitrary rule to work. It also enables an efficient parallelization under an additional assumption. We then show that existing resampling oracles for perfect matchings and permutations do satisfy this condition. #R##N#Finally, we generalize the precondition in [2] (in the case of symmetric potential causality graphs). This unifies special cases that previously were treated separately.

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"
    - "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 "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"
    - "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-3 (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"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "entropy optimization and counting"

Connected through hyperedge HE-4 (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"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "local computation mechanism design"

Connected through hyperedge HE-5 (induced by source paper "a local lemma for focused stochastic algorithms"):
  with members:
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (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"
    - "algorithmic and enumerative aspects of the moser tardos distribution"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "partial resampling to approximate covering integer programs"

Connected through hyperedge HE-8 (induced by source paper "variable version lov asz local lemma beyond shearer s bound"):
  with members:
    - "lopsidependency in the moser tardos framework beyond the lopsided lovasz local lemma"
    - "deterministic parallel algorithms for fooling polylogarithmic juntas and the lovasz local lemma"
    - "uniform sampling through the lov asz local lemma"
    - "computing the independence polynomial in shearer s region for the lll"
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "focused stochastic local search and the lov asz 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"

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)