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 probabilistic calculus of actions"
  Abstract: We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which represents the observation X = x, and causal conditioning, P(y|do(X = x)), read the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions (and policies) from partially specified knowledge bases, such as Bayesian networks in which some conditional probabilities may not be available.

Connected through hyperedge HE-1 (induced by source paper "algorithmic recourse from counterfactual explanations to interventions"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "preserving causal constraints in counterfactual explanations for machine learning classifiers"
    - "face feasible and actionable counterfactual explanations"
    - "efficient search for diverse coherent explanations"

Connected through hyperedge HE-2 (induced by source paper "domain adaptation as a problem of inference on graphical models"):
  with members:
    - "detecting and correcting for label shift with black box predictors"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "deep transfer learning with joint adaptation networks"
    - "coupled generative adversarial networks"
    - "deep coral correlation alignment for deep domain adaptation"
    - "domain separation networks"
    - "learning transferable features with deep adaptation networks"
    - "generative moment matching networks"

Connected through hyperedge HE-3 (induced by source paper "identifiability and transportability in dynamic causal networks"):
  with members:
    - "an efficient algorithm for computing interventional distributions in latent variable causal models"
    - "identifying conditional causal effects"
    - "learning why things change the difference based causality learner"
    - "discovering cyclic causal models by independent components analysis"

Connected through hyperedge HE-4 (induced by source paper "context specific independence in bayesian networks"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "causal independence for knowledge acquisition and inference"
    - "on heuristics for finding loop cutsets in multiply connected belief networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"

Connected through hyperedge HE-5 (induced by source paper "probabilities of causation bounds and identification"):
  with members:
    - "axiomatizing causal reasoning"
    - "testing identifiability of causal effects"
    - "decision theoretic troubleshooting a framework for repair and experiment"
    - "psychological and normative theories of causal power and the probabilities of causes"
    - "counterfactuals and policy analysis in structural models"

Connected through hyperedge HE-6 (induced by source paper "probabilistic evaluation of sequential plans from causal models with hidden variables"):
  with members:
    - "testing identifiability of causal effects"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-7 (induced by source paper "modeling failure priors and persistence in model based diagnosis"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"
    - "a polynomial algorithm for computing the optimal repair strategy in a system with independent component failures"

Connected through hyperedge HE-8 (induced by source paper "counterfactual probabilities computational methods bounds and applications"):
  with members:
    - "from conditional oughts to qualitative decision theory"

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)