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 "viterbi training in prism"):
  Abstract: VT (Viterbi training), or hard EM, is an efficient way of parameter learning for probabilistic models with hidden variables. Given an observation y, it searches for a state of hidden variables x that maximizes p(x, y | θ) by coordinate ascent on parameters θ. In this paper we introduce VT to PRISM, a logic-based probabilistic modeling system for generative models. VT improves PRISM’s probabilistic modeling in two ways. First although generative models are said to be inappropriate for discrimination tasks in general, when parameters are learned by VT, models often show good discrimination performance. We conducted two parsing experiments with probabilistic grammars while learning parameters by a variety of inference methods, i.e. VT,EM,MAP and VB. The result is that VT achieves the best parsing accuracy among them in both experiments. Second since VT always deals with a single probability of a single explanation, Viterbi explanation, the exclusiveness condition imposed on PRISM programs is no more required when we learn parameters by VT. PRISM with VT thus allows us to write inclusive clause bodies, learn parameters and compute Viterbi explanations.

Includes members:
  Member "the pita system tabling and answer subsumption for reasoning under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "value of information in probabilistic logic programs")
  Member "linear tabling strategies and optimizations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tabled typeclass resolution")
  Member "parameter learning of logic programs for symbolic statistical modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "value of information in probabilistic logic programs")
  Member "efficient probabilistic top down and left corner parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "in order transition based constituent parsing")

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)