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: "search based methods to bound diagnostic probabilities in very large belief nets"
  Abstract: Since exact probabilistic inference is intractable in general for large multiply connected belief nets, approximate methods are required. A promising approach is to use heuristic search among hypotheses (instantiations of the network) to find the most probable ones, as in the TopN algorithm. Search is based on the relative probabilities of hypotheses which are efficient to compute. Given upper and lower bounds on the relative probability of partial hypotheses, it is possible to obtain bounds on the absolute probabilities of hypotheses. Best-first search aimed at reducing the maximum error progressively narrows the bounds as more hypotheses are examined. Here, qualitative probabilistic analysis is employed to obtain bounds on the relative probability of partial hypotheses for the BN20 class of networks networks and a generalization replacing the noisy OR assumption by negative synergy. The approach is illustrated by application to a very large belief network, QMR-BN, which is a reformulation of the Internist-1 system for diagnosis in internal medicine.

Connected through hyperedge HE-1 (induced by source paper "real time top k predictive query processing over event streams"):
  with members:
    - "evidence absorption and propagation through evidence reversals"
    - "a bayesian approach to learning causal networks"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-2 (induced by source paper "independence of causal influence and clique tree propagation"):
  with members:
    - "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"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "fast belief update using order of magnitude probabilities"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "parallel belief revision"
    - "the use of conflicts in searching bayesian networks"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "local expression languages for probabilistic dependence a preliminary report"

Connected through hyperedge HE-4 (induced by source paper "optimal monte carlo estimation of belief network inference"):
  with members:
    - "improved sampling for diagnostic reasoning in bayesian networks"
    - "backward simulation in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-5 (induced by source paper "efficient search based inference for noisy or belief networks topepsilon"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "some properties of joint probability distributions"
    - "the use of conflicts in searching bayesian networks"
    - "a new look at causal independence"
    - "knowledge engineering for large belief networks"

Connected through hyperedge HE-6 (induced by source paper "computational complexity reduction for bn2o networks using similarity of states"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "a new look at causal independence"
    - "knowledge engineering for large belief networks"
    - "state space abstraction for anytime evaluation of probabilistic networks"

Connected through hyperedge HE-7 (induced by source paper "context specific approximation in probabilistic inference"):
  with members:
    - "context specific independence in bayesian networks"
    - "localized partial evaluation of belief networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-8 (induced by source paper "incremental probabilistic inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "advances in probabilistic reasoning"
    - "local expression languages for probabilistic dependence a preliminary report"

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)