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: "on the complexity and approximation of binary evidence in lifted inference"
  Abstract: Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They show impressive performance when calculating unconditional probabilities in relational models, but often resort to non-lifted inference when computing conditional probabilities. The reason is that conditioning on evidence breaks many of the model's symmetries, which can preempt standard lifting techniques. Recent theoretical results show, for example, that conditioning on evidence which corresponds to binary relations is #P-hard, suggesting that no lifting is to be expected in the worst case. In this paper, we balance this negative result by identifying the Boolean rank of the evidence as a key parameter for characterizing the complexity of conditioning in lifted inference. In particular, we show that conditioning on binary evidence with bounded Boolean rank is efficient. This opens up the possibility of approximating evidence by a low-rank Boolean matrix factorization, which we investigate both theoretically and empirically.

Connected through hyperedge HE-1 (induced by source paper "taming reasoning in temporal probabilistic relational models"):
  with members:
    - "lifted probabilistic inference for asymmetric graphical models"
    - "tractable inference for complex stochastic processes"
    - "lifted variable elimination decoupling the operators from the constraint language"

Connected through hyperedge HE-2 (induced by source paper "boolean matrix factorization meets consecutive ones property"):
  with members:
    - "the primping routine tiling through proximal alternating linearized minimization"
    - "c salt mining class specific alterations in boolean matrix factorization"

Connected through hyperedge HE-3 (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"
    - "symmetry aware marginal density estimation"
    - "object oriented bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "lifted marginal map inference"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "map complexity results and approximation methods"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-5 (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"
    - "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 "symmetric weighted first order model counting"):
  with members:
    - "lower complexity bounds for lifted inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "skolemization for weighted first order model counting"
    - "symmetry aware marginal density estimation"
    - "probabilistic databases with markoviews"
    - "algebraic model counting"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-7 (induced by source paper "lifted tree reweighted variational inference"):
  with members:
    - "automorphism groups of graphical models and lifted variational 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"

Connected through hyperedge HE-8 (induced by source paper "tractability through exchangeability a new perspective on efficient probabilistic inference"):
  with members:
    - "exchangeable variable models"
    - "context specific independence in bayesian networks"
    - "symmetry aware marginal density estimation"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "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)