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: "decision theoretic foundations for causal reasoning"
  Abstract: We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "directed reduction algorithms and decomposable graphs"
    - "a transformational characterization of equivalent bayesian network structures"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "the incentives that shape behaviour"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "ai safety gridworlds"
    - "on formalizing fairness in prediction with machine learning"
    - "a game theoretic analysis of the off switch game"
    - "good and safe uses of ai oracles"

Connected through hyperedge HE-4 (induced by source paper "von neumann morgenstern and savage theorems for causal decision making"):
  with members:
    - "playing against nature causal discovery for decision making under uncertainty"
    - "building machines that learn and think like people"
    - "identifying optimal sequential decisions"

Connected through hyperedge HE-5 (induced by source paper "online causal structure learning in the presence of latent variables"):
  with members:
    - "causal inference in the presence of latent variables and selection bias"
    - "finding optimal bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "axiomatizing causal reasoning"):
  with members:
    - "causality in bayesian belief networks"
    - "defining explanation in probabilistic systems"

Connected through hyperedge HE-7 (induced by source paper "a graph theoretic analysis of information value"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"
    - "reformulating inference problems through selective conditioning"
    - "efficient decision theoretic planning techniques and empirical analysis"
    - "ideal reformulation of belief networks"
    - "dynamic construction of belief networks"
    - "chain graphs for learning"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "representing context sensitive knowledge in a network formalism a preliminary report"

Connected through hyperedge HE-8 (induced by source paper "causes and explanations a structural model approach part 1 causes"):
  with members:
    - "axiomatizing causal reasoning"
    - "counterfactual probabilities computational methods bounds and applications"
    - "probabilities of causation bounds and identification"
    - "responsibility and blame a structural model approach"
    - "causes and explanations a structural model approach part ii explanations"

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)