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: "lazy propagation in junction trees"
  Abstract: The efficiency of algorithms using secondary structures for probabilistic inference in Bayesian networks can be improved by exploiting independence relations induced by evidence and the direction of the links in the original network. In this paper we present an algorithm that on-line exploits independence relations induced by evidence and the direction of the links in the original network to reduce both time and space costs. Instead of multiplying the conditional probability distributions for the various cliques, we determine on-line which potentials to multiply when a message is to be produced. The performance improvement of the algorithm is emphasized through empirical evaluations involving large real world Bayesian networks, and we compare the method with the HUGIN and Shafer-Shenoy inference algorithms.

Connected through hyperedge HE-1 (induced by source paper "lazy evaluation of symmetric bayesian decision problems"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "d separation from theorems to algorithms"
    - "a method for using belief networks as influence diagrams"

Connected through hyperedge HE-2 (induced by source paper "inference in multiply sectioned bayesian networks with extended shafer shenoy and lazy propagation"):
  with members:
    - "object oriented bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "exploiting functional dependence in bayesian network inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "causal independence for knowledge acquisition and inference"
    - "multiplicative factorization of noisy max"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "identifying the relevant nodes without learning the model"):
  with members:
    - "strong completeness and faithfulness in bayesian networks"
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "constructing situation specific belief networks"
    - "computational advantages of relevance reasoning in bayesian belief networks"
    - "finding optimal bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "probability and asset updating using bayesian networks for combinatorial prediction markets"):
  with members:
    - "price updating in combinatorial prediction markets with bayesian networks"
    - "complexity of combinatorial market makers"

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)