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 "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.

Includes members:
  Member "incremental knowledge base construction using deepdive":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint reasoning for multi faceted commonsense knowledge")
  Member "dimmwitted a study of main memory statistical analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed learning of deep neural networks using independent subnet training")
  Member "projecting markov random field parameters for fast mixing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "maximum likelihood learning with arbitrary treewidth via fast mixing parameter sets")
  Member "complexity of inference in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies")
  Member "tractable hypergraph properties for constraint satisfaction and conjunctive queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")

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.LG(Machine Learning)