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: "automorphism groups of graphical models and lifted variational inference"
  Abstract: Using the theory of group action, we first introduce the concept of the automorphism group of an exponential family or a graphical model, thus formalizing the general notion of symmetry of a probabilistic model. This automorphism group provides a precise mathematical framework for lifted inference in the general exponential family. Its group action partitions the set of random variables and feature functions into equivalent classes (called orbits) having identical marginals and expectations. Then the inference problem is effectively reduced to that of computing marginals or expectations for each class, thus avoiding the need to deal with each individual variable or feature. We demonstrate the usefulness of this general framework in lifting two classes of variational approximation for MAP inference: local LP relaxation and local LP relaxation with cycle constraints; the latter yields the first lifted inference that operate on a bound tighter than local constraints. Initial experimental results demonstrate that lifted MAP inference with cycle constraints achieved the state of the art performance, obtaining much better objective function values than local approximation while remaining relatively efficient.

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"
    - "markov chains on orbits of permutation groups"
    - "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 "block value symmetries in probabilistic graphical models"):
  with members:
    - "counting belief propagation"

Connected through hyperedge HE-4 (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"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"
    - "lifted graphical models a survey"

Connected through hyperedge HE-5 (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"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-6 (induced by source paper "lifted message passing for the generalized belief propagation"):
  with members:
    - "first order decomposition trees"
    - "practical graph isomorphism ii"
    - "on the choice of regions for generalized belief propagation"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"

Connected through hyperedge HE-7 (induced by source paper "lifted region based belief propagation"):
  with members:
    - "lifted tree reweighted variational inference"
    - "template based inference in symmetric relational markov random fields"
    - "counting belief propagation"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-8 (induced by source paper "lifted tree reweighted variational inference"):
  with members:
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "template based inference in symmetric relational markov random fields"
    - "tightening lp relaxations for map using message passing"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "convergent propagation algorithms via oriented trees"
    - "a new class of upper bounds on the log partition function"
    - "probabilistic theorem proving"

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)