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 local lemma for focused stochastic algorithms"
  Abstract: We develop a framework for the rigorous analysis of focused stochastic local search algorithms. These are algorithms that search a state space by repeatedly selecting some constraint that is violated in the current state and moving to a random nearby state that addresses the violation, while hopefully not introducing many new ones. An important class of focused local search algorithms with provable performance guarantees has recently arisen from algorithmizations of the Lov\'{a}sz Local Lemma (LLL), a non-constructive tool for proving the existence of satisfying states by introducing a background measure on the state space. While powerful, the state transitions of algorithms in this class must be, in a precise sense, perfectly compatible with the background measure. In many applications this is a very restrictive requirement and one needs to step outside the class. Here we introduce the notion of \emph{measure distortion} and develop a framework for analyzing arbitrary focused stochastic local search algorithms, recovering LLL algorithmizations as the special case of no distortion. Our framework takes as input an arbitrary such algorithm and an arbitrary probability measure and shows how to use the measure as a yardstick of algorithmic progress, even for algorithms designed independently of the measure.

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"
    - "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 "beyond the lovasz local lemma point to set correlations and their algorithmic applications"):
  with members:
    - "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"
    - "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"
    - "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"

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)