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: "incremental probabilistic inference"
  Abstract: Propositional representation services such as truth maintenance systems offer powerful support for incremental, interleaved, problem-model construction and evaluation. Probabilistic inference systems, in contrast, have lagged behind in supporting this incrementality typically demanded by problem solvers. The problem, we argue, is that the basic task of probabilistic inference is typically formulated at too large a grain-size. We show how a system built around a smaller grain-size inference task can have the desired incrementality and serve as the basis for a low-level (propositional) probabilistic representation service.

Connected through hyperedge HE-1 (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"
    - "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"

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"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "ideal reformulation of belief networks"
    - "refinement and coarsening of bayesian networks"
    - "ideal a software package for analysis of influence diagrams"

Connected through hyperedge HE-3 (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"
    - "state space abstraction for anytime evaluation of probabilistic networks"

Connected through hyperedge HE-4 (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"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "localized partial evaluation of belief networks"

Connected through hyperedge HE-5 (induced by source paper "policy recognition in the abstract hidden markov model"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "the automated mapping of plans for plan recognition"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "accounting for context in plan recognition with application to traffic monitoring"
    - "tractable inference for complex stochastic processes"
    - "a new model of plan recognition"
    - "probabilistic state dependent grammars for plan recognition"
    - "context specific independence in bayesian 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)