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 "belief propagation for structured decision making"):
  Abstract: Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both theoretically and empirically.

Includes members:
  Member "decision making using probabilistic inference methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "influence diagrams for the optimization of a vehicle speed profile")
  Member "probabilistic inference in influence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solving multistage influence diagrams using branch and bound search")
  Member "solving influence diagrams using hugin shafer shenoy and lazy propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "solving limited memory influence diagrams using branch and bound search")
  Member "from influence diagrams to junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a symbolic algebra for the computation of expected utilities in multiplicative influence diagrams")
  Member "map estimation linear programming and belief propagation with convex free energies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convergence rates of smooth message passing with rounding in entropy regularized map inference")
  Member "a new class of upper bounds on the log partition function":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual semantic information pursuit a survey")
  Member "join graph propagation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning mcmc proposals")

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)