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: "general purpose mcmc inference over relational structures"
  Abstract: Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carlo (MCMC) techniques with customized proposal distributions. Currently, implementing such a system requires coding MCMC state representations and acceptance probability calculations that are specific to a particular application. An alternative approach, which we pursue in this paper, is to use a general-purpose probabilistic modeling language (such as BLOG) and a generic Metropolis-Hastings MCMC algorithm that supports user-supplied proposal distributions. Our algorithm gains flexibility by using MCMC states that are only partial descriptions of possible worlds; we provide conditions under which MCMC over partial worlds yields correct answers to queries. We also show how to use a context-specific Bayes net to identify the factors in the acceptance probability that need to be computed for a given proposed move. Experimental results on a citation matching task show that our general-purpose MCMC engine compares favorably with an application-specific system.

Connected through hyperedge HE-1 (induced by source paper "swift compiled inference for probabilistic programming languages"):
  with members:
    - "simpl a dsl for automatic specialization of inference algorithms"
    - "c3 lightweight incrementalized mcmc for probabilistic programs using continuations and callsite caching"
    - "approximate bayesian image interpretation using generative probabilistic graphics programs"
    - "church a language for generative models"
    - "gibbs sampling in open universe stochastic languages"

Connected through hyperedge HE-2 (induced by source paper "herded gibbs sampling"):
  with members:
    - "church a language for generative models"
    - "herding dynamic weights for partially observed random field models"
    - "nonparametric bayesian logic"

Connected through hyperedge HE-3 (induced by source paper "markov chains on orbits of permutation groups"):
  with members:
    - "lower complexity bounds for lifted inference"
    - "practical graph isomorphism ii"
    - "a delayed column generation strategy for exact k bounded map inference in markov logic networks"
    - "bisimulation based approximate lifted inference"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "symmetry breaking answer set solving"

Connected through hyperedge HE-4 (induced by source paper "gibbs sampling in open universe stochastic languages"):
  with members:
    - "church a language for generative models"

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.AI(Artificial Intelligence)