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: "object oriented bayesian networks"
  Abstract: Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies. Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN--particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts--can also be used to speed up the inference process.

Connected through hyperedge HE-1 (induced by source paper "probabilistic software modeling a data driven paradigm for software analysis"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "density estimation using real nvp"
    - "gaussian error linear units gelus"
    - "made masked autoencoder for distribution estimation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "structured factored inference a framework for automated reasoning in probabilistic programming languages"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-5 (induced by source paper "hinge loss markov random fields and probabilistic soft logic"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "exploiting social network structure for person to person sentiment analysis"
    - "inferring user preferences by probabilistic logical reasoning over social networks"
    - "tighter linear program relaxations for high order graphical models"
    - "hinge loss markov random fields convex inference for structured prediction"
    - "a hypergraph partitioned vertex programming approach for large scale consensus optimization"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "algebraic model counting"

Connected through hyperedge HE-6 (induced by source paper "relational approach to knowledge engineering for pomdp based assistance systems as a translation of a psychological model"):
  with members:
    - "a new look at causal independence"
    - "spudd stochastic planning using decision diagrams"
    - "probabilistic theorem proving"

Connected through hyperedge HE-7 (induced by source paper "network fragments representing knowledge for constructing probabilistic models"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "causal independence for knowledge acquisition and inference"
    - "model based influence diagrams for machine vision"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "utility based control for computer vision"

Connected through hyperedge HE-8 (induced by source paper "hypothesis management in situation specific network construction"):
  with members:
    - "constructing situation specific belief networks"
    - "generating bayesian networks from probability logic knowledge bases"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a probabilistic analysis of marker passing techniques for plan recognition"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "computational advantages of relevance reasoning in bayesian belief 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)