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: "stochastic logic programs sampling inference and applications"
  Abstract: Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are presented, based respectively, on variable elimination and importance sampling. We then show how SLPs can be used to represent prior distributions for machine learning, using (i) logic programs and (ii) Bayes net structures as examples. Drawing on existing work in statistics, we apply the Metropolis-Hasting algorithm to construct a Markov chain which samples from the posterior distribution. A Prolog implementation for this is described. We also discuss the possibility of constructing explicit representations of the posterior.

Connected through hyperedge HE-1 (induced by source paper "constraint based inference in probabilistic logic programs"):
  with members:
    - "inference in probabilistic logic programs using lifted explanations"
    - "new liftable classes for first order probabilistic inference"
    - "lifted variable elimination for probabilistic logic programming"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "logical hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "adaptive mcmc based inference in probabilistic logic programs"):
  with members:
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"

Connected through hyperedge HE-3 (induced by source paper "markov chain monte carlo using tree based priors on model structure"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "being bayesian about network structure"

Connected through hyperedge HE-4 (induced by source paper "on the implementation of the probabilistic logic programming language problog"):
  with members:
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-5 (induced by source paper "cp logic a language of causal probabilistic events and its relation to logic programming"):
  with members:
    - "using first order probability logic for the construction of bayesian networks"
    - "relational bayesian networks"
    - "structure based causes and explanations in the independent choice logic"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "probabilistic reasoning with answer sets"
    - "a logic for non monotone inductive definitions"
    - "causes and explanations a structural model approach part ii explanations"
    - "bayesian logic programs"

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)