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 "on the approximability of weighted model integration on dnf structures"):
  Abstract: Weighted model counting admits an FPRAS on DNF structures. We study weighted model integration, which is a generalization of weighted model counting, and pose the following question: Does weighted model integration on DNF structures admit an FPRAS? Building on classical results, we show that this problem can indeed be approximated for a class of weight functions. Our approximation algorithm is based on three subroutines, each of which can be a weak (i.e., approximate), or a strong (i.e., exact) oracle, and in all cases, comes along with accuracy guarantees. We experimentally verify our approach, and show that our algorithm scales to large problem instances, which are currently out of reach for existing, general-purpose weighted model integration solvers.

Includes members:
  Member "learning to reason leveraging neural networks for approximate dnf counting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective")
  Member "on hashing based approaches to approximate dnf counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to reason leveraging neural networks for approximate dnf counting")
  Member "geodesic walks in polytopes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal anytime regret with two experts")
  Member "inference and learning in probabilistic logic programs using weighted boolean formulas":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence")
  Member "distribution aware sampling and weighted model counting for sat":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting database management systems and treewidth for counting")
  Member "approximate counting in smt and value estimation for probabilistic programs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")
  Member "approximate inference algorithms for hybrid bayesian networks with discrete constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling transportation routines using hybrid dynamic mixed networks")
  Member "probabilistic theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")

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)