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: "some properties of joint probability distributions"
  Abstract: Several Artificial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncertain domain models. Even though the correct working of these schemes is practically contingent upon the existence of a small number of probable states, no formal justification has been proposed of why this should be the case. This paper attempts to fill this apparent gap by studying asymmetries among probabilities of various states of uncertain models. By rewriting the joint probability distribution over a model's variables into a product of individual variables' prior and conditional probability distributions, and applying central limit theorem to this product, we can demonstrate that the probabilities of individual states of the model can be expected to be drawn from highly skewed, log-normal distributions. With sufficient asymmetry in individual prior and conditional probability distributions, a small fraction of states can be expected to cover a large portion of the total probability space with the remaining states having practically negligible probability. Theoretical discussion is supplemented by simulation results and an illustrative real-world example.

Connected through hyperedge HE-1 (induced by source paper "flexible policy construction by information refinement"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "the compilation of decision models"
    - "an empirical evaluation of a randomized algorithm for probabilistic inference"
    - "deliberation scheduling for time critical sequential decision making"
    - "two procedures for compiling influence diagrams"
    - "exploiting causal independence in bayesian network inference"
    - "provably bounded optimal agents"

Connected through hyperedge HE-2 (induced by source paper "sample and accumulate algorithms for belief updating in bayes networks"):
  with members:
    - "the use of conflicts in searching bayesian networks"
    - "a stratified simulation scheme for inference in bayesian belief networks"
    - "backward simulation in bayesian networks"
    - "a new look at causal independence"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "incremental probabilistic inference"

Connected through hyperedge HE-3 (induced by source paper "an anytime algorithm for decision making under uncertainty"):
  with members:
    - "decision making using probabilistic inference methods"
    - "utility based abstraction and categorization"
    - "reasoning about the value of decision model refinement methods and application"
    - "the compilation of decision models"
    - "flexible policy construction by information refinement"
    - "two procedures for compiling influence diagrams"

Connected through hyperedge HE-4 (induced by source paper "efficient search based inference for noisy or belief networks topepsilon"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "the use of conflicts in searching bayesian networks"
    - "a new look at causal independence"
    - "knowledge engineering for large belief 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)