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 bayesian approach to constraint based causal inference"
  Abstract: We target the problem of accuracy and robustness in causal inference from finite data sets. Some state-of-the-art algorithms produce clear output complete with solid theoretical guarantees but are susceptible to propagating erroneous decisions, while others are very adept at handling and representing uncertainty, but need to rely on undesirable assumptions. Our aim is to combine the inherent robustness of the Bayesian approach with the theoretical strength and clarity of constraint-based methods. We use a Bayesian score to obtain probability estimates on the input statements used in a constraint-based procedure. These are subsequently processed in decreasing order of reliability, letting more reliable decisions take precedence in case of con icts, until a single output model is obtained. Tests show that a basic implementation of the resulting Bayesian Constraint-based Causal Discovery (BCCD) algorithm already outperforms established procedures such as FCI and Conservative PC. It can also indicate which causal decisions in the output have high reliability and which do not.

Connected through hyperedge HE-1 (induced by source paper "neuropathic pain diagnosis simulator for causal discovery algorithm evaluation"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "quantifying generalization in reinforcement learning"
    - "learning to drive from simulation without real world labels"
    - "residual attention network for image classification"
    - "evaluating causal models by comparing interventional distributions"
    - "diagnostic prediction using discomfort drawings with ibtm"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "distinguishing cause from effect using observational data methods and benchmarks"

Connected through hyperedge HE-2 (induced by source paper "ancestral causal inference"):
  with members:
    - "clingo asp control preliminary report"
    - "causal discovery from changes"
    - "cyclic causal discovery from continuous equilibrium data"
    - "a logical characterization of constraint based causal discovery"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
    - "adjacency faithfulness and conservative causal inference"

Connected through hyperedge HE-3 (induced by source paper "a sound and complete algorithm for learning causal models from relational data"):
  with members:
    - "reasoning about independence in probabilistic models of relational data"
    - "causal inference and causal explanation with background knowledge"
    - "operations for learning with graphical models"

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)