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: "a new perspective on stochastic local search and the lovasz local lemma"
  Abstract: We present a new perspective on the analysis of stochastic local search algorithms via linear algebra. Our key insight is that LLL-inspired convergence arguments can be seen as a method for bounding the spectral radius of a matrix specifying the algorithm to be analyzed. Armed with this viewpoint we give a unified analysis of all \emph{entropy compression} applications, connecting backtracking algorithms to the LLL in the same fashion that existing analyses connect resampling algorithms to the LLL. We then give a new convergence condition that seamlessly handles resampling algorithms that can detect, and back away from, unfavorable parts of the state space. We give several applications of this condition, notably a new vertex coloring algorithm for arbitrary graphs that uses a number of colors that matches the algorithmic barrier for random graphs. Finally, we introduce a generalization of Kolmogorov's notion of \emph{commutative} algorithms, cast as matrix commutativity, which affords much simpler proofs both of the original results and of recent extensions.

Connected through hyperedge HE-1 (induced by source paper "efficiently list edge coloring multigraphs asymptotically optimally"):
  with members:
    - "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"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "entropy optimization and counting"

Connected through hyperedge HE-2 (induced by source paper "local computation algorithms for the lovasz local lemma"):
  with members:
    - "on the complexity of local distributed graph problems"
    - "commutativity in the algorithmic lovasz local lemma"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "local computation mechanism design"

Connected through hyperedge HE-3 (induced by source paper "commutative algorithms approximate the lll distribution"):
  with members:
    - "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"
    - "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"

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)