human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dynamic mcmc sampling"):
  Abstract: The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from graphical models, e.g. Markov random fields (MRF). Traditional MCMC sampling algorithms are focused on a classic static setting, where the input is fixed. In this paper we study the problem of sampling from an MRF when the graphical model itself is changing dynamically with time. The problem is well motivated by the growing volume and velocity of data in today's applications of the MCMC methods. #R##N#For the two major MCMC approaches, respectively for the approximate and perfect sampling, namely, the Gibbs sampling and the coupling from the past (CFTP), we give dynamic versions for the respective MCMC sampling algorithms. On MRF with $n$ variables and bounded maximum degrees, these dynamic sampling algorithms can maintain approximate or perfect samples, while the MRF is dynamically changing. Furthermore, our algorithms are efficient with $\tilde{O}(n)$ space cost, and $\tilde{O}(\log^2n)$ incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains. These decay conditions were well known in the literature of couplings for rapid mixing of Markov chains, and now for the first time, are used to imply efficient dynamic sampling algorithms. Consequently, we have efficient dynamic sampling algorithms for the following models: #R##N#(1) general MRF satisfying the Dobrushin-Shlosman condition (for approximate sampling); #R##N#(2) Ising model with temperature $\beta$ where $\mathrm{e}^{-2|\beta|}> 1-\frac{2}{\Delta+1}$ (for both approximate and perfect samplings); #R##N#(3) hardcore model with fugacity $\lambda<\frac{2}{\Delta-2}$ (for both approximate and perfect samplings); #R##N#(4) proper $q$-coloring with: $q>2\Delta$ (for approximate sampling); or $q>2\Delta^2+3\Delta$ (for perfect sampling).

Includes members:
  Member "uniform sampling through the lov asz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic sampling from graphical models")
  Member "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectral independence in high dimensional expanders and applications to the hardcore model")
  Member "inapproximability of the partition function for the antiferromagnetic ising and hard core models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lower bounds for testing graphical models colorings and antiferromagnetic ising models")
  Member "correlation decay up to uniqueness in spin systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral independence in high dimensional expanders and applications to the hardcore model")

Question: Please tell me which class the source paper 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)