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 "randomized algorithms for the loop cutset problem"):
  Abstract: We show how to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized algorithm for finding a loop cutset outputs a minimum loop cutset after O(c 6kkn) steps with probability at least 1 - (1 - 1/6k)c6k, where c > 1 is a constant specified by the user, k is the minimal size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm often finds a loop cutset that is closer to the minimum weight loop cutset than the ones found by the best deterministic algorithms known.

Includes members:
  Member "bounded conditioning flexible inference for decisions under scarce resources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using qualitative relationships for bounding probability distributions")
  Member "ideal reformulation of belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "metareasoning for planning under uncertainty")
  Member "on the logic of causal models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic reasoning across the causal hierarchy")
  Member "causal networks semantics and expressiveness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "global conditioning for probabilistic inference in belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "query dags a practical paradigm for implementing belief network inference")

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)