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: "reasoning about independence in probabilistic models of relational data"
  Abstract: Bayesian networks leverage conditional independence to compactly encode joint probability distributions. Many learning algorithms exploit the constraints implied by observed conditional independencies to learn the structure of Bayesian networks. The rules of d-separation provide a theoretical and algorithmic framework for deriving conditional independence facts from model structure. However, this theory only applies to Bayesian networks. Many realworld systems, such as social or economic systems, are characterized by interacting heterogeneous entities and probabilistic dependencies that cross the boundaries of entities. Consequently, researchers have developed extensions to Bayesian networks that can represent these relational dependencies. We show that the theory of d-separation inaccurately infers conditional independence when applied directly to the structure of probabilistic models of relational data. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally ecient method for answering d-separation queries about relational models, and we present empirical results that demonstrate eectiveness.

Connected through hyperedge HE-1 (induced by source paper "probabilistic relational model benchmark generation"):
  with members:
    - "a sound and complete algorithm for learning causal models from relational data"

Connected through hyperedge HE-2 (induced by source paper "lifted representation of relational causal models revisited implications for reasoning and structure learning"):
  with members:
    - "a sound and complete algorithm for learning causal models from relational data"
    - "adjacency faithfulness and conservative causal inference"

Connected through hyperedge HE-3 (induced by source paper "a sound and complete algorithm for learning causal models from relational data"):
  with members:
    - "causal inference and causal explanation with background knowledge"
    - "a bayesian approach to constraint based causal inference"
    - "operations for learning with graphical models"

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)