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: "context specific approximation in probabilistic inference"
  Abstract: There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewer distinctions. Unfortunately, the level of a Bayesian network seems too coarse; it is unlikely that a parent will make little difference for all values of the other parents. In this paper we consider an approximation scheme where distinctions can be ignored in some contexts, but not in other contexts. We elaborate on a notion of a parent context that allows a structured context-specific decomposition of a probability distribution and the associated probabilistic inference scheme called probabilistic partial evaluation (Poole 1997). This paper shows a way to simplify a probabilistic model by ignoring distinctions which have similar probabilities, a method to exploit the simpler model, a bound on the resulting errors, and some preliminary empirical results on simple networks.

Connected through hyperedge HE-1 (induced by source paper "representing and combining partially specified cpts"):
  with members:
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"
    - "constructing situation specific belief networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "computing upper and lower bounds on likelihoods in intractable networks"

Connected through hyperedge HE-2 (induced by source paper "learning bayesian networks with restricted causal interactions"):
  with members:
    - "bayesian learning of loglinear models for neural connectivity"
    - "structure and parameter learning for causal independence and causal interaction models"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "inter causal independence and heterogeneous factorization"
    - "context specific independence in bayesian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "active tuples based scheme for bounding posterior beliefs"):
  with members:
    - "large deviation methods for approximate probabilistic inference"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "knowledge engineering for large belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "accuracy bounds for belief propagation"
    - "approximate decomposition a method for bounding and estimating probabilistic and deterministic queries"
    - "an empirical study of w cutset sampling for bayesian networks"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-4 (induced by source paper "exploiting contextual independence in probabilistic inference"):
  with members:
    - "exploiting the rule structure for decision making within the independent choice logic"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "learning bayesian networks with local structure"
    - "a new look at causal independence"
    - "context specific independence in bayesian networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "correlated action effects in decision theoretic regression"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-5 (induced by source paper "when do numbers really matter"):
  with members:
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "learning bayesian nets that perform well"
    - "making sensitivity analysis computationally efficient"
    - "analysing sensitivity data from probabilistic networks"

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)