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: "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.

Connected through hyperedge HE-1 (induced by source paper "surrogate outcomes and transportability"):
  with members:
    - "causal inference by surrogate experiments z identifiability"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-2 (induced by source paper "causal transfer learning"):
  with members:
    - "ancestral causal inference"
    - "joint causal inference from multiple contexts"
    - "causal discovery from changes"
    - "cyclic causal discovery from continuous equilibrium data"
    - "on causal and anticausal learning"
    - "domain adaptation learning bounds and algorithms"

Connected through hyperedge HE-3 (induced by source paper "joint causal inference from multiple contexts"):
  with members:
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "ancestral causal inference"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "strong completeness and faithfulness in bayesian networks"
    - "learning sparse causal models is not np hard"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal discovery from changes"
    - "causal discovery from a mixture of experimental and observational data"

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)