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: "pearl s calculus of intervention is complete"
  Abstract: This paper is concerned with graphical criteria that can be used to solve the problem of identifying casual effects from nonexperimental data in a causal Bayesian network structure, i.e., a directed acyclic graph that represents causal relationships. We first review Pearl's work on this topic [Pearl, 1995], in which several useful graphical criteria are presented. Then we present a complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem. By exploiting the completeness of this algorithm, we prove that the three basic do-calculus rules that Pearl presents are complete, in the sense that, if a causal effect is identifiable, there exists a sequence of applications of the rules of the do-calculus that transforms the causal effect formula into a formula that only includes observational quantities.

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"
    - "on the logic of causal models"

Connected through hyperedge HE-2 (induced by source paper "separators and adjustment sets in causal graphs complete criteria and an algorithmic framework"):
  with members:
    - "an algorithm for finding minimum d separating sets in belief networks"
    - "identification of conditional interventional distributions"
    - "adjustment criteria in causal diagrams an algorithmic perspective"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-3 (induced by source paper "surrogate outcomes and transportability"):
  with members:
    - "a general algorithm for deciding transportability of experimental results"
    - "causal inference by surrogate experiments z identifiability"

Connected through hyperedge HE-4 (induced by source paper "causal inference by string diagram surgery"):
  with members:
    - "the logical essentials of bayesian reasoning"
    - "disintegration and bayesian inversion both abstractly and concretely"

Connected through hyperedge HE-5 (induced by source paper "decomposition and identification of linear structural equation models"):
  with members:
    - "generalized instrumental variables"

Connected through hyperedge HE-6 (induced by source paper "a factorization criterion for acyclic directed mixed graphs"):
  with members:
    - "local markov property for models satisfying composition axiom"

Connected through hyperedge HE-7 (induced by source paper "a general algorithm for deciding transportability of experimental results"):
  with members:
    - "axiomatizing causal reasoning"
    - "testing identifiability of causal effects"
    - "direct and indirect effects"
    - "probabilistic evaluation of sequential plans from causal models with hidden variables"
    - "identification of conditional interventional distributions"
    - "causal inference by surrogate experiments z identifiability"

Connected through hyperedge HE-8 (induced by source paper "causal transportability of experiments on controllable subsets of variables z transportability"):
  with members:
    - "testing identifiability of causal effects"
    - "identification of conditional interventional distributions"
    - "causal inference by surrogate experiments z identifiability"
    - "identifying conditional causal effects"
    - "the do calculus revisited"

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)