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: "learning bayesian nets that perform well"
  Abstract: A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for learning BNs, however, attempt to optimize another criterion (usually likelihood, possibly augmented with a regularizing term), which is independent of the distribution of queries that are posed. This paper takes the "performance criteria" seriously, and considers the challenge of computing the BN whose performance - read "accuracy over the distribution of queries" - is optimal. We show that many aspects of this learning task are more difficult than the corresponding subtasks in the standard model.

Connected through hyperedge HE-1 (induced by source paper "bayesian error bars for belief net inference"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "a differential approach to inference in bayesian networks"
    - "minimal assumption distribution propagation in belief networks"
    - "an implementation of a method for computing the uncertainty in inferred probabilities in belief networks"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "estimating well performing bayesian networks using bernoulli mixtures"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks with local structure"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "mixture approximations to bayesian networks"
    - "being bayesian about network structure"

Connected through hyperedge HE-3 (induced by source paper "exploiting qualitative knowledge in the learning of conditional probabilities of bayesian networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"
    - "accelerating em an empirical study"
    - "update rules for parameter estimation in bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "comparing bayesian network classifiers"):
  with members:
    - "induction of selective bayesian classifiers"

Connected through hyperedge HE-5 (induced by source paper "on the number of samples needed to learn the correct structure of a bayesian network"):
  with members:
    - "learning factor graphs in polynomial time sample complexity"

Connected through hyperedge HE-6 (induced by source paper "when do numbers really matter"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "analysing sensitivity data from probabilistic 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)