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: "generating bayesian networks from probability logic knowledge bases"
  Abstract: We present a method for dynamically generating Bayesian networks from knowledge bases consisting of first-order probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network. We define the concept of d-separation for knowledge bases and prove that a knowledge base with independence conditions defined by d-separation is a complete specification of a probability distribution. We present a network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P(Q|E). We prove the algorithm to be correct.

Connected through hyperedge HE-1 (induced by source paper "survey on models and techniques for root cause analysis"):
  with members:
    - "learning possibilistic logic theories from default rules"
    - "learning tractable probabilistic models for fault localization"
    - "using abduction in markov logic networks for root cause analysis"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "evaluating computational models of explanation using human judgments"

Connected through hyperedge HE-2 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages"):
  with members:
    - "on the semantics and complexity of probabilistic logic programs"
    - "new liftable classes for first order probabilistic inference"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "lower complexity bounds for lifted inference"
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "query dags a practical paradigm for implementing belief network inference"
    - "a simple fptas for counting edge covers"

Connected through hyperedge HE-3 (induced by source paper "lazy factored inference for functional probabilistic programming"):
  with members:
    - "pruning bayesian networks for efficient computation"
    - "expectation propagation for approximate bayesian inference"
    - "dynamic construction of belief networks"
    - "church a language for generative models"
    - "an empirical evaluation of possible variations of lazy propagation"

Connected through hyperedge HE-4 (induced by source paper "probabilistic logic programming under inheritance with overriding"):
  with members:
    - "belief functions and default reasoning"
    - "credal networks under maximum entropy"
    - "relational bayesian networks"
    - "hybrid probabilistic programs algorithms and complexity"
    - "from statistical knowledge bases to degrees of belief"
    - "another perspective on default reasoning"
    - "nonmonotonic reasoning preferential models and cumulative logics"
    - "probabilistic default reasoning with conditional constraints"

Connected through hyperedge HE-5 (induced by source paper "constructing situation specific belief networks"):
  with members:
    - "pruning bayesian networks for efficient computation"
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "d separation from theorems to algorithms"
    - "integrating model construction and evaluation"
    - "tradeoffs in constructing and evaluating temporal influence diagrams"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-6 (induced by source paper "credal networks under maximum entropy"):
  with members:
    - "coherent knowledge processing at maximum entropy by spirit"
    - "decision making with interval influence diagrams"
    - "irrelevance and independence relations in quasi bayesian networks"
    - "from statistical knowledge bases to degrees of belief"
    - "probabilistic default reasoning with conditional constraints"
    - "probabilistic deduction with conditional constraints over basic events"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-7 (induced by source paper "hypothesis management in situation specific network construction"):
  with members:
    - "constructing situation specific belief networks"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a probabilistic analysis of marker passing techniques for plan recognition"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-8 (induced by source paper "defining explanation in probabilistic systems"):
  with members:
    - "causality in bayesian belief networks"
    - "counterfactual probabilities computational methods bounds and applications"
    - "relevant explanations allowing disjunctive assignments"
    - "from statistical knowledge bases to degrees of belief"
    - "decision theoretic foundations for causal reasoning"

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)