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 simple parallel and distributed sampling technique local glauber dynamics"
  Abstract: \emph{Sampling} constitutes an important tool in a variety of areas: from machine learning and combinatorial optimization to computational physics and biology. A central class of sampling algorithms is the \emph{Markov Chain Monte Carlo} method, based on the construction of a Markov chain with the desired sampling distribution as its stationary distribution. Many of the traditional Markov chains, such as the \emph{Glauber dynamics}, do not scale well with increasing dimension. To address this shortcoming, we propose a simple local update rule based on the Glauber dynamics that leads to efficient parallel and distributed algorithms for sampling from Gibbs distributions. #R##N#Concretely, we present a Markov chain that mixes in $O(\log n)$ rounds when Dobrushin's condition for the Gibbs distribution is satisfied. This improves over the \emph{LubyGlauber} algorithm by Feng, Sun, and Yin [PODC'17], which needs $O(\Delta \log n)$ rounds, and their \emph{LocalMetropolis} algorithm, which converges in $O(\log n)$ rounds but requires a considerably stronger mixing condition. Here, $n$ denotes the number of nodes in the graphical model inducing the Gibbs distribution, and $\Delta$ its maximum degree. In particular, our method can sample a uniform proper coloring with $(2+\eps)\Delta$ colors in $O(\log n)$ rounds, for any constant $\eps >0$, which almost matches the threshold of the sequential Glauber dynamics and improves on the $(2 +\sqrt{2}+\eps)\Delta$ threshold of Feng et al.

Connected through hyperedge HE-1 (induced by source paper "local access to huge random objects through partial sampling"):
  with members:
    - "on the probe complexity of local computation algorithms"
    - "sublinear random access generators for preferential attachment graphs"
    - "parallel algorithms for generating random networks with given degree sequences"
    - "exact recovery in the stochastic block model"
    - "fast local computation algorithms"

Connected through hyperedge HE-2 (induced by source paper "distributed metropolis sampler with optimal parallelism"):
  with members:
    - "dynamic sampling from graphical models"
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "hogwild gibbs can be panaccurate"
    - "what can be sampled locally"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-3 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "what can be sampled locally"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-4 (induced by source paper "dynamic sampling from graphical models"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "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"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"

Connected through hyperedge HE-5 (induced by source paper "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"):
  with members:
    - "on local distributed sampling and counting"
    - "what can be sampled locally"
    - "sampling random colorings of sparse random graphs"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "on the complexity of local distributed graph problems"
    - "the locality of distributed symmetry breaking"
    - "deterministic distributed vertex coloring in polylogarithmic time"

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)