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 "domain recursion for lifted inference with existential quantifiers"):
  Abstract: In recent work, we proved that the domain recursion inference rule makes domain-lifted inference possible on several relational probability models (RPMs) for which the best known time complexity used to be exponential. We also identified two classes of RPMs for which inference becomes domain lifted when using domain recursion. These two classes subsume the largest lifted classes that were previously known. In this paper, we show that domain recursion can also be applied to models with existential quantifiers. Currently, all lifted inference algorithms assume that existential quantifiers have been removed in pre-processing by Skolemization. We show that besides introducing potentially inconvenient negative weights, Skolemization may increase the time complexity of inference. We give two example models where domain recursion can replace Skolemization, avoids the need for dealing with negative numbers, and reduces the time complexity of inference. These two examples may be interesting from three theoretical aspects: 1- they provide a better and deeper understanding of domain recursion and, in general, (lifted) inference, 2- they may serve as evidence that there are larger classes of models for which domain recursion can satisfyingly replace Skolemization, and 3- they may serve as evidence that better Skolemization techniques exist.

Includes members:
  Member "why is compiling lifted inference into a low level language so effective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fusing first order knowledge compilation and the lifted junction tree algorithm")
  Member "automorphism groups of graphical models and lifted variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "markov chains on orbits of permutation groups":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lifted hybrid variational inference")
  Member "probabilistic theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")
  Member "towards completely lifted search based probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "lifted graphical models a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")

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)