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 "scaling up probabilistic inference in linear and non linear hybrid domains by leveraging knowledge compilation"):
  Abstract: Weighted model integration (WMI) extends weighted model counting (WMC) in providing a computational abstraction for probabilistic inference in mixed discrete-continuous domains. WMC has emerged as an assembly language for state-of-the-art reasoning in Bayesian networks, factor graphs, probabilistic programs and probabilistic databases. In this regard, WMI shows immense promise to be much more widely applicable, especially as many real-world applications involve attribute and feature spaces that are continuous and mixed. Nonetheless, state-of-the-art tools for WMI are limited and less mature than their propositional counterparts. #R##N#In this work, we propose a new implementation regime that leverages propositional knowledge compilation for scaling up inference. In particular, we use sentential decision diagrams, a tractable representation of Boolean functions, as the underlying model counting and model enumeration scheme. Our regime performs competitively to state-of-the-art WMI systems, but is also shown, for the first time, to handle non-linear constraints over non-linear potentials.

Includes members:
  Member "quantifying program bias":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning probabilistic logic programs in continuous domains")
  Member "probabilistic inference modulo theories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling up hybrid probabilistic inference with logical and arithmetic constraints via message passing")
  Member "inference and learning in probabilistic logic programs using weighted boolean formulas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence")
  Member "approximate counting in smt and value estimation for probabilistic programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")
  Member "algebraic model counting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")
  Member "symbolic dynamic programming for discrete and continuous state mdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "making decisions with spatially and temporally uncertain data")
  Member "sum product networks a new deep architecture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "training deep energy based models with f divergence minimization")
  Member "solving sat and bayesian inference with backtracking search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")

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)