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 causal independence in bayesian network inference"):
  Abstract: A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as ``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.

Includes members:
  Member "pruning bayesian networks for efficient computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lazy factored inference for functional probabilistic programming")
  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 "a new look at causal independence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation")
  Member "parameter adjustment in bayes networks the generalized noisy or gate":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfense online accelerated defense against adversarial deep learning")
  Member "additive belief network models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forecasting sleep apnea with dynamic network models")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "d separation from theorems to algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "query the model precomputations for efficient inference with bayesian networks")
  Member "knowledge engineering for large belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "independence of causal influence and clique tree propagation")

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)