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 "generating and sampling orbits for lifted probabilistic inference"):
  Abstract: Lifted inference scales to large probability models by exploiting symmetry. However, existing exact lifted inference techniques do not apply to general factor graphs, as they require a relational representation. In this work we provide a theoretical framework and algorithm for performing exact lifted inference on symmetric factor graphs by computing colored graph automorphisms, as is often done for approximate lifted inference. Our key insight is to represent variable assignments directly in the colored factor graph encoding. This allows us to generate representatives and compute the size of each orbit of the symmetric distribution. In addition to exact inference, we use this encoding to implement an MCMC algorithm that explores the space of orbits quickly by uniform orbit sampling.

Includes members:
  Member "tractability through exchangeability a new perspective on efficient probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational pooling for graph representations")
  Member "lifted probabilistic inference for asymmetric graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "taming reasoning in temporal probabilistic relational models")
  Member "symmetry aware marginal density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "practical graph isomorphism ii":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors")
  Member "automorphism groups of graphical models and lifted variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "markov chains on orbits of permutation groups":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lifted hybrid variational inference")
  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)