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: "causal inference by surrogate experiments z identifiability"
  Abstract: We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation. This problem, which we call z-identifiability, reduces to ordinary identifiability when Z = empty and, like the latter, can be given syntactic characterization using the do-calculus [Pearl, 1995; 2000]. We provide a graphical necessary and sufficient condition for z-identifiability for arbitrary sets X,Z, and Y (the outcomes). We further develop a complete algorithm for computing the causal effect of X on Y using information provided by experiments on Z. Finally, we use our results to prove completeness of do-calculus relative to z-identifiability, a result that does not follow from completeness relative to ordinary identifiability.

Connected through hyperedge HE-1 (induced by source paper "surrogate outcomes and transportability"):
  with members:
    - "a general algorithm for deciding transportability of experimental results"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-2 (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"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-3 (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"
    - "pearl s calculus of intervention is complete"
    - "identifying conditional causal effects"
    - "the do calculus revisited"

Connected through hyperedge HE-4 (induced by source paper "the do calculus revisited"):
  with members:
    - "direct and indirect effects"
    - "pearl s calculus of intervention is complete"

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)