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: "loglinear models for first order probabilistic reasoning"
  Abstract: Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a conservative extension of first-order reasoning, so that, for example, there is a one-one mapping between logical and random variables. We show how, in this framework, Inductive Logic Programming (ILP) can be used to induce the features of a loglinear model from data. We also compare the presented framework with other approaches to first-order probabilistic reasoning.

Connected through hyperedge HE-1 (induced by source paper "probabilistic reasoning with answer sets"):
  with members:
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the dlv system for knowledge representation and reasoning"
    - "from statistical knowledge bases to degrees of belief"
    - "weight constraints as nested expressions"
    - "bayesian logic programs"

Connected through hyperedge HE-2 (induced by source paper "relational dynamic bayesian networks"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "context specific independence in bayesian networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "relational bayesian networks"
    - "factored particles for scalable monitoring"
    - "the ff planning system fast plan generation through heuristic search"
    - "bayesian logic programs"

Connected through hyperedge HE-3 (induced by source paper "parameter learning of logic programs for symbolic statistical modeling"):
  with members:
    - "from statistical knowledge bases to degrees of belief"
    - "linear tabulated resolution based on prolog control strategy"
    - "inside outside estimation of a lexicalized pcfg for german"
    - "probabilistic deduction with conditional constraints over basic events"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "bayesian logic programs"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "context specific independence in bayesian networks"
    - "relational bayesian networks"

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)