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: "state space abstraction for anytime evaluation of probabilistic networks"
  Abstract: One important factor determining the computational complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency. We present an anytime procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the procedure exhibits a smooth improvement in approximation quality as computation time increases. This suggests that state-space abstraction is one more useful control parameter for designing real-time probabilistic reasoners.

Connected through hyperedge HE-1 (induced by source paper "independence of causal influence and clique tree propagation"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "utility based abstraction and categorization"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "causal independence for knowledge acquisition and inference"
    - "the use of conflicts in searching bayesian networks"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "abstraction in belief networks the role of intermediate states in diagnostic reasoning"

Connected through hyperedge HE-2 (induced by source paper "a graph theoretic analysis of information value"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"
    - "reformulating inference problems through selective conditioning"
    - "efficient decision theoretic planning techniques and empirical analysis"
    - "ideal reformulation of belief networks"
    - "dynamic construction of belief networks"
    - "chain graphs for learning"
    - "representing context sensitive knowledge in a network formalism a preliminary report"
    - "localized partial evaluation of belief networks"

Connected through hyperedge HE-3 (induced by source paper "using qualitative relationships for bounding probability distributions"):
  with members:
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "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"
    - "localized partial evaluation of belief networks"
    - "propagation of 2 monotone lower probabilities on an undirected graph"
    - "incremental probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "sample and accumulate algorithms for belief updating in bayes networks"):
  with members:
    - "some properties of joint probability distributions"
    - "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"
    - "incremental probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "some experiments with real time decision algorithms"):
  with members:
    - "the use of conflicts in searching bayesian networks"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "fast belief update using order of magnitude probabilities"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "localized partial evaluation of belief networks"
    - "incremental probabilistic inference"

Connected through hyperedge HE-6 (induced by source paper "learning the dimensionality of hidden variables"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "reasoning about the value of decision model refinement methods and application"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "refinement and coarsening of bayesian networks"
    - "the bayesian structural em algorithm"

Connected through hyperedge HE-7 (induced by source paper "computational complexity reduction for bn2o networks using similarity of states"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "a new look at causal independence"
    - "knowledge engineering for large belief networks"

Connected through hyperedge HE-8 (induced by source paper "incremental tradeoff resolution in qualitative probabilistic networks"):
  with members:
    - "using qualitative relationships for bounding probability distributions"
    - "reasoning with qualitative probabilities can be tractable"
    - "refining reasoning in qualitative probabilistic networks"
    - "exploiting causal independence in bayesian network inference"

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)