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: "bounded conditioning flexible inference for decisions under scarce resources"
  Abstract: We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final probabilities of interest with the allocation of a complete resource fraction. The approach allows a reasoner to exchange arbitrary quantities of computational resource for incremental gains in inference quality. As such, bounded conditioning holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning resources. The algorithm solves a probabilistic bounding problem in complex belief networks by breaking the problem into a set of mutually exclusive, tractable subproblems and ordering their solution by the expected effect that each subproblem will have on the final answer. We introduce the algorithm, discuss its characterization, and present its performance on several belief networks, including a complex model for reasoning about problems in intensive-care medicine.

Connected through hyperedge HE-1 (induced by source paper "using qualitative relationships for bounding probability distributions"):
  with members:
    - "incremental tradeoff resolution in qualitative probabilistic networks"
    - "robustness analysis of bayesian networks with local convex sets of distributions"
    - "path planning under time dependent uncertainty"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "localized partial evaluation of belief networks"
    - "propagation of 2 monotone lower probabilities on an undirected graph"
    - "incremental probabilistic inference"

Connected through hyperedge HE-2 (induced by source paper "state space abstraction for anytime evaluation of probabilistic networks"):
  with members:
    - "utility based abstraction and categorization"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "using tree decomposable structures to approximate belief networks"
    - "ideal reformulation of belief networks"
    - "refinement and coarsening of bayesian networks"
    - "ideal a software package for analysis of influence diagrams"
    - "incremental probabilistic inference"

Connected through hyperedge HE-3 (induced by source paper "some experiments with real time decision algorithms"):
  with members:
    - "the use of conflicts in searching bayesian networks"
    - "fast belief update using order of magnitude probabilities"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "localized partial evaluation of belief networks"
    - "incremental probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "fast belief update using order of magnitude probabilities"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "parallel belief revision"
    - "the use of conflicts in searching bayesian networks"
    - "local expression languages for probabilistic dependence a preliminary report"

Connected through hyperedge HE-5 (induced by source paper "a combination of cutset conditioning with clique tree propagation in the pathfinder system"):
  with members:
    - "a constraint propagation approach to probabilistic reasoning"

Connected through hyperedge HE-6 (induced by source paper "active tuples based scheme for bounding posterior beliefs"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "large deviation methods for approximate probabilistic inference"
    - "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-7 (induced by source paper "mean field methods for a special class of belief networks"):
  with members:
    - "large deviation methods for approximate probabilistic inference"
    - "variational probabilistic inference and the qmr dt network"
    - "variational cumulant expansions for intractable distributions"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-8 (induced by source paper "randomized algorithms for the loop cutset problem"):
  with members:
    - "ideal reformulation of belief networks"
    - "on the logic of causal models"
    - "causal networks semantics and expressiveness"
    - "global conditioning for probabilistic inference in 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)