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 "hypertree decompositions revisited for pgms"):
  Abstract: We revisit the classical problem of exact inference on probabilistic graphical models (PGMs). Our algorithm is based on recent worst-case optimal database join algorithms, which can be asymptotically faster than traditional data processing methods. We present the first empirical evaluation of these new algorithms via JoinInfer, a new exact inference engine. We empirically explore the properties of the data for which our engine can be expected to outperform traditional inference engines refining current theoretical notions. Further, JoinInfer outperforms existing state-of-the-art inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by up to a factor of 630x. Finally, we propose a promising data-driven heuristic that extends JoinInfer to automatically tailor its parameters and/or switch to the traditional inference algorithms.

Includes members:
  Member "size bounds and query plans for relational joins":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "general and fractional hypertree decompositions hard and easy cases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "faq questions asked frequently":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "skew strikes back new developments in the theory of join algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast join project query evaluation using matrix multiplication")
  Member "join graph propagation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning mcmc proposals")
  Member "exploiting contextual independence in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "addmc exact weighted model counting with algebraic decision diagrams")
  Member "exploiting causal independence in bayesian network inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)