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: "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
  Abstract: Gibbs sampling on factor graphs is a widely used inference technique, which often produces good empirical results. Theoretical guarantees for its performance are weak: even for tree structured graphs, the mixing time of Gibbs may be exponential in the number of variables. To help understand the behavior of Gibbs sampling, we introduce a new (hyper)graph property, called hierarchy width. We show that under suitable conditions on the weights, bounded hierarchy width ensures polynomial mixing time. Our study of hierarchy width is in part motivated by a class of factor graph templates, hierarchical templates, which have bounded hierarchy width---regardless of the data used to instantiate them. We demonstrate a rich application from natural language processing in which Gibbs sampling provably mixes rapidly and achieves accuracy that exceeds human volunteers.

Connected through hyperedge HE-1 (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"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "hogwild gibbs can be panaccurate"
    - "what can be sampled locally"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (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"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "what can be sampled locally"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-3 (induced by source paper "what can be sampled locally"):
  with members:
    - "on local distributed sampling and counting"
    - "distributed degree splitting edge coloring and orientations"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "an exponential separation between randomized and deterministic complexity in the local model"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"

Connected through hyperedge HE-4 (induced by source paper "holoclean holistic data repairs with probabilistic inference"):
  with members:
    - "truth finding on the deep web is the problem solved"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "incremental knowledge base construction using deepdive"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "dimmwitted a study of main memory statistical analytics"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-5 (induced by source paper "ensuring rapid mixing and low bias for asynchronous gibbs sampling"):
  with members:
    - "frogwild fast pagerank approximations on graph engines"
    - "incremental knowledge base construction using deepdive"
    - "taming the wild a unified analysis of hogwild style algorithms"
    - "dimmwitted a study of main memory statistical analytics"

Connected through hyperedge HE-6 (induced by source paper "scan order in gibbs sampling models in which it matters and bounds on how much"):
  with members:
    - "the madlib analytics library or mad skills the sql"

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.LG(Machine Learning)