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 "parameter learning of logic programs for symbolic statistical modeling"):
  Abstract: We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks.#R##N##R##N#We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs.#R##N##R##N#The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.

Includes members:
  Member "loglinear models for first order probabilistic reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic reasoning with answer sets")
  Member "from statistical knowledge bases to degrees of belief":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polynomial time probabilistic reasoning with partial observations via implicit learning in probability logics")
  Member "linear tabulated resolution based on prolog control strategy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tabled typeclass resolution")
  Member "inside outside estimation of a lexicalized pcfg for german":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lexicalized stochastic modeling of constraint based grammars using log linear measures and em training")
  Member "probabilistic deduction with conditional constraints over basic events":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic reasoning in the description logic alcp with the principle of maximum entropy full version")
  Member "exploiting causal independence in bayesian network inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)