human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "exploiting functional dependence in bayesian network inference"):
  Abstract: We propose an efficient method for Bayesian network inference in models with functional dependence. We generalize the multiplicative factorization method originally designed by Takikawa and D Ambrosio(1999) FOR models WITH independence OF causal influence.Using a hidden variable, we transform a probability potential INTO a product OF two - dimensional potentials.The multiplicative factorization yields more efficient inference. FOR example, IN junction tree propagation it helps TO avoid large cliques. IN ORDER TO keep potentials small, the number OF states OF the hidden variable should be minimized.We transform this problem INTO a combinatorial problem OF minimal base IN a particular space.We present an example OF a computerized adaptive test, IN which the factorization method IS significantly more efficient than previous inference methods.

Includes members:
  Member "a tractable inference algorithm for diagnosing multiple diseases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network")
  Member "causal independence for knowledge acquisition and inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks")
  Member "multiplicative factorization of noisy max":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lifted variable elimination for probabilistic logic programming")
  Member "a new look at causal independence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation")
  Member "a generalization of the noisy or model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks")
  Member "lazy propagation in junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lazy evaluation of symmetric bayesian decision problems")
  Member "exploiting causal independence in bayesian network inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an advance on variable elimination with applications to tensor based computation")

Question: Please tell me which class the source paper 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)