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 "oblivious bounds on the probability of boolean functions"):
  Abstract: This article develops upper and lower bounds for the probability of Boolean functions by treating multiple occurrences of variables as independent and assigning them new individual probabilities. We call this approach dissociation and give an exact characterization of optimal oblivious bounds, that is, when the new probabilities are chosen independently of the probabilities of all other variables. Our motivation comes from the weighted model counting problem (or, equivalently, the problem of computing the probability of a Boolean function), which is #P-hard in general. By performing several dissociations, one can transform a Boolean formula whose probability is difficult to compute into one whose probability is easy to compute, and which is guaranteed to provide an upper or lower bound on the probability of the original formula by choosing appropriate probabilities for the dissociated variables. Our new bounds shed light on the connection between previous relaxation-based and model-based approximations and unify them as concrete choices in a larger design space. We also show how our theory allows a standard relational database management system (DBMS) to both upper and lower bound hard probabilistic queries in guaranteed polynomial time.

Includes members:
  Member "dissociation and propagation for approximate lifted inference with standard relational database management systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty annotated databases a lightweight approach for approximating certain answers extended version")
  Member "dissociation and propagation for efficient query evaluation over probabilistic databases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate lifted inference with probabilistic databases")
  Member "structured message passing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faq questions asked frequently")
  Member "approximation by quantization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structured message passing")
  Member "formula based probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational hybridization and transformation for large inaccurate noisy or networks")
  Member "node splitting a scheme for generating upper bounds in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dissociation and propagation for approximate lifted inference with standard relational database management systems")
  Member "optimal upper and lower bounds for boolean expressions by dissociation":
    also connected through other hyperedges:
      (none)
  Member "a knowledge compilation map":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abstracting probabilistic models a logical perspective")

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)