human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "markov chains on orbits of permutation groups"
  Abstract: We present a novel approach to detecting and utilizing symmetries in probabilistic graphical models with two main contributions. First, we present a scalable approach to computing generating sets of permutation groups representing the symmetries of graphical models. Second, we introduce orbital Markov chains, a novel family of Markov chains leveraging model symmetries to reduce mixing times. We establish an insightful connection between model symmetries and rapid mixing of orbital Markov chains. Thus, we present the first lifted MCMC algorithm for probabilistic graphical models. Both analytical and empirical results demonstrate the effectiveness and efficiency of the approach.

Connected through hyperedge HE-1 (induced by source paper "generating and sampling orbits for lifted probabilistic inference"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "symmetry aware marginal density estimation"
    - "practical graph isomorphism ii"
    - "automorphism groups of graphical models and lifted variational inference"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-3 (induced by source paper "lifted marginal map inference"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "map complexity results and approximation methods"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-4 (induced by source paper "coarse to fine lifted map inference in computer vision"):
  with members:
    - "learning tractable probabilistic models for fault localization"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-5 (induced by source paper "domain recursion for lifted inference with existential quantifiers"):
  with members:
    - "why is compiling lifted inference into a low level language so effective"
    - "automorphism groups of graphical models and lifted variational inference"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"
    - "lifted graphical models a survey"

Connected through hyperedge HE-6 (induced by source paper "contextual symmetries in probabilistic graphical models"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-7 (induced by source paper "lifted message passing for the generalized belief propagation"):
  with members:
    - "first order decomposition trees"
    - "practical graph isomorphism ii"
    - "automorphism groups of graphical models and lifted variational inference"
    - "on the choice of regions for generalized belief propagation"
    - "counting belief propagation"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"

Connected through hyperedge HE-8 (induced by source paper "new liftable classes for first order probabilistic inference"):
  with members:
    - "why is compiling lifted inference into a low level language so effective"
    - "relational bayesian networks"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"

Question: Please tell me which class the center node 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)