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: "multiplicative factorization of noisy max"
  Abstract: The noisy-or and its generalization noisy-max have been utilized to reduce the complexity of knowledge acquisition. In this paper, we present a new representation of noisy-max that allows for efficient inference in general Bayesian networks. Empirical studies show that our method is capable of computing queries in well-known large medical networks, QMR-DT and CPCS, for which no previous exact inference method has been shown to perform well.

Connected through hyperedge HE-1 (induced by source paper "lifted variable elimination for probabilistic logic programming"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "skolemization for weighted first order model counting"
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "constraint processing in lifted probabilistic inference"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-2 (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"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "lazy propagation in junction trees"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-3 (induced by source paper "real time inference with large scale temporal bayes nets"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "exploiting structure in weighted model counting approaches to probabilistic inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "causal independence for knowledge acquisition and inference"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "context specific independence in bayesian networks"
    - "exploiting evidence in probabilistic inference"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (induced by source paper "truncating the loop series expansion for belief propagation"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "structured region graphs morphing ep into gbp"
    - "a new class of upper bounds on the log partition function"
    - "loop calculus helps to improve belief propagation and linear programming decodings of low density parity check codes"
    - "loop corrections for approximate inference"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-6 (induced by source paper "loop corrections for approximate inference"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "structured region graphs morphing ep into gbp"
    - "variational probabilistic inference and the qmr dt network"

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)