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: "on the conditional logic of simulation models"
  Abstract: We propose analyzing conditional reasoning by appeal to a notion of intervention on a simulation program, formalizing and subsuming a number of approaches to conditional thinking in the recent AI literature. Our main results include a series of axiomatizations, allowing comparison between this framework and existing frameworks (normality-ordering models, causal structural equation models), and a complexity result establishing NP-completeness of the satisfiability problem. Perhaps surprisingly, some of the basic logical principles common to all existing approaches are invalidated in our causal simulation approach. We suggest that this additional flexibility is important in modeling some intuitive examples.

Connected through hyperedge HE-1 (induced by source paper "probabilistic reasoning across the causal hierarchy"):
  with members:
    - "on open universe causal reasoning"
    - "pyro deep universal probabilistic programming"
    - "causal modeling with probabilistic simulation models"
    - "building machines that learn and think like people"
    - "axiomatizing causal reasoning"
    - "on the logic of causal models"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-2 (induced by source paper "on open universe causal reasoning"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "building machines that learn and think like people"
    - "axiomatizing causal reasoning"
    - "learning the structure of dynamic probabilistic networks"
    - "church a language for generative models"
    - "nonparametric bayesian logic"
    - "towards common sense reasoning via conditional simulation legacies of turing in artificial intelligence"
    - "probabilistic theorem proving"

Connected through hyperedge HE-3 (induced by source paper "causal modeling with probabilistic simulation models"):
  with members:
    - "axiomatizing causal reasoning"

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.LO(Logic in Computer Science)