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 "a general algorithm for deciding transportability of experimental results"):
  Abstract: Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called “transportability”, defined as a license to transfer information learned in experimental studies to a different population, on which only observational studies can be conducted. Given a set of assumptions concerning commonalities and differences between the two populations, Pearl and Bareinboim [1] derived sufficient conditions that permit such transfer to take place. This article summarizes their findings and supplements them with an effective procedure for deciding when and how transportability is feasible. It establishes a necessary and sufficient condition for deciding when causal effects in the target population are estimable from both the statistical information available and the causal information transferred from the experiments. The article further provides a complete algorithm for computing the transport formula, that is, a way of combining observational and experimental information to synthesize bias-free estimate of the desired causal relation. Finally, the article examines the differences between transportability and other variants of generalizability.

Includes members:
  Member "axiomatizing causal reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficiently learning and sampling interventional distributions from observations")
  Member "testing identifiability of causal effects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficiently learning and sampling interventional distributions from observations")
  Member "direct and indirect effects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased scene graph generation from biased training")
  Member "probabilistic evaluation of sequential plans from causal models with hidden variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficiently learning and sampling interventional distributions from observations")
  Member "identification of conditional interventional distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weighted tensor completion for time series causal inference")
  Member "causal inference by surrogate experiments z identifiability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "surrogate outcomes and transportability")
  Member "pearl s calculus of intervention is complete":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic reasoning across the causal hierarchy")

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)