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: "bayesian logic programs"
  Abstract: Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developed

Connected through hyperedge HE-1 (induced by source paper "anytime exact belief propagation"):
  with members:
    - "probabilistic inference modulo theories"
    - "accuracy bounds for belief propagation"

Connected through hyperedge HE-2 (induced by source paper "inference and learning in probabilistic logic programs using weighted boolean formulas"):
  with members:
    - "inference in probabilistic logic programs using weighted cnf s"
    - "well definedness and efficient inference for probabilistic logic programming under the distribution semantics"
    - "on the implementation of the probabilistic logic programming language problog"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "robot location estimation in the situation calculus"):
  with members:
    - "reasoning about probabilities in dynamic systems using goal regression"
    - "action networks a framework for reasoning about actions and change under uncertainty"
    - "monitoring a complez physical system using a hybrid dynamic bayes net"
    - "probabilistic reasoning with answer sets"
    - "reasoning about noisy sensors and effectors in the situation calculus"

Connected through hyperedge HE-4 (induced by source paper "probabilistic inductive logic programming based on answer set programming"):
  with members:
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-5 (induced by source paper "location based reasoning about complex multi agent behavior"):
  with members:
    - "relational bayesian networks"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"
    - "logical hidden markov models"
    - "monitoring teams by overhearing a multi agent plan recognition approach"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-6 (induced by source paper "model checking with probabilistic tabled logic programming"):
  with members:
    - "inference in probabilistic logic programs with continuous random variables"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "the magic of logical inference in probabilistic programming"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "model checking probabilistic pushdown automata"

Connected through hyperedge HE-7 (induced by source paper "a new characterization of probabilities in bayesian networks"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "generating bayesian networks from probability logic knowledge bases"
    - "dynamic construction of belief networks"
    - "optimal junction trees"
    - "relational bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "inference in probabilistic logic programs with continuous random variables"):
  with members:
    - "parameter learning in prism programs with continuous random variables"
    - "the magic of logical inference in probabilistic programming"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "parameter learning of logic programs for symbolic statistical modeling"

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)