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: "on the logic of causal models"
  Abstract: This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of conditional independence relationships. We show that DAGs offer polynomially sound and complete inference mechanisms for inferring conditional independence relationships from a given causal set of such relationships. As a consequence, d-separation, a graphical criterion for identifying independencies in a DAG, is shown to uncover more valid independencies then any other criterion. In addition, we employ the Armstrong property of conditional independence to show that the dependence relationships displayed by a DAG are inherently consistent, i.e. for every DAG D there exists some probability distribution P that embodies all the conditional independencies displayed in D and none other.

Connected through hyperedge HE-1 (induced by source paper "probabilistic reasoning across the causal hierarchy"):
  with members:
    - "on open universe causal reasoning"
    - "pyro deep universal probabilistic programming"
    - "causal modeling with probabilistic simulation models"
    - "on the conditional logic of simulation models"
    - "building machines that learn and think like people"
    - "axiomatizing causal reasoning"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-2 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "ai safety gridworlds"
    - "on formalizing fairness in prediction with machine learning"
    - "a game theoretic analysis of the off switch game"
    - "good and safe uses of ai oracles"

Connected through hyperedge HE-3 (induced by source paper "dependence and relevance a probabilistic view"):
  with members:
    - "causal networks semantics and expressiveness"
    - "separable and transitive graphoids"

Connected through hyperedge HE-4 (induced by source paper "reasoning about independence in probabilistic models of relational data"):
  with members:
    - "a factorization criterion for acyclic directed mixed graphs"
    - "causal networks semantics and expressiveness"
    - "directed cyclic graphical representations of feedback models"
    - "a sound and complete algorithm for learning causal models from relational data"
    - "causal inference in the presence of latent variables and selection bias"
    - "discriminative probabilistic models for relational data"
    - "a logical characterization of constraint based causal discovery"
    - "operations for learning with graphical models"

Connected through hyperedge HE-5 (induced by source paper "a graph based inference method for conditional independence"):
  with members:
    - "causal networks semantics and expressiveness"
    - "d separation from theorems to algorithms"
    - "a constraint propagation approach to probabilistic reasoning"

Connected through hyperedge HE-6 (induced by source paper "on separation criterion and recovery algorithm for chain graphs"):
  with members:
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "chain graphs for learning"

Connected through hyperedge HE-7 (induced by source paper "on testing whether an embedded bayesian network represents a probability model"):
  with members:
    - "causal networks semantics and expressiveness"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-8 (induced by source paper "causal networks semantics and expressiveness"):
  with members:
    - "a constraint propagation approach to probabilistic reasoning"
    - "intelligent probabilistic inference"

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)