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: "relational bayesian networks"
  Abstract: A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network. By using a powerful way of specifying conditional probability distributions in these networks, the resulting formalism is more expressive than the previous ones. Particularly, it provides for constraints on equalities of events, and it allows to define complex, nested combination functions.

Connected through hyperedge HE-1 (induced by source paper "review on graph feature learning and feature extraction techniques for link prediction"):
  with members:
    - "link prediction based on graph neural networks"
    - "dual convolutional neural network for graph of graphs link prediction"
    - "hierarchical graph representation learning with differentiable pooling"
    - "temporally evolving community detection and prediction in content centric networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "temporally evolving community detection and prediction in content centric networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-2 (induced by source paper "mebn rm a mapping between multi entity bayesian network and relational model"):
  with members:
    - "the ctu prague relational learning repository"
    - "discriminative probabilistic models for relational data"

Connected through hyperedge HE-3 (induced by source paper "probabilistic complex event recognition a survey"):
  with members:
    - "going deeper into action recognition a survey"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "a probabilistic logic programming event calculus"
    - "lifted relational neural networks"
    - "complex events recognition under uncertainty in a sensor network"
    - "location based reasoning about complex multi agent behavior"
    - "a model for reasoning with uncertain rules in event composition systems"
    - "probabilistic event calculus for event recognition"

Connected through hyperedge HE-4 (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-5 (induced by source paper "new liftable classes for first order probabilistic inference"):
  with members:
    - "why is compiling lifted inference into a low level language so effective"
    - "markov chains on orbits of permutation groups"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"

Connected through hyperedge HE-6 (induced by source paper "why is compiling lifted inference into a low level language so effective"):
  with members:
    - "constraint processing in lifted probabilistic inference"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-7 (induced by source paper "lower complexity bounds for lifted inference"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "discriminative probabilistic models for relational data"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "bisimulation based approximate lifted inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-8 (induced by source paper "dynamic bayesian ontology languages"):
  with members:
    - "the complexity of reasoning with cardinality restrictions and nominals in expressive description logics"

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)