human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a factorization criterion for acyclic directed mixed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structural learning of multivariate regression chain graphs via decomposition")
  Member "on the logic of causal models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic reasoning across the causal hierarchy")
  Member "causal networks semantics and expressiveness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "directed cyclic graphical representations of feedback models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "characterizing distribution equivalence for cyclic and acyclic directed graphs")
  Member "a sound and complete algorithm for learning causal models from relational data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards robust relational causal discovery")
  Member "causal inference in the presence of latent variables and selection bias":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "discriminative probabilistic models for relational data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning distributional programs for relational autocompletion")
  Member "a logical characterization of constraint based causal discovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint causal inference from multiple contexts")

Question: Please tell me which class the source paper 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)