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: "towards completely lifted search based probabilistic inference"
  Abstract: The promise of lifted probabilistic inference is to carry out probabilistic inference in a relational probabilistic model without needing to reason about each individual separately (grounding out the representation) by treating the undistinguished individuals as a block. Current exact methods still need to ground out in some cases, typically because the representation of the intermediate results is not closed under the lifted operations. We set out to answer the question as to whether there is some fundamental reason why lifted algorithms would need to ground out undifferentiated individuals. We have two main results: (1) We completely characterize the cases where grounding is polynomial in a population size, and show how we can do lifted inference in time polynomial in the logarithm of the population size for these cases. (2) For the case of no-argument and single-argument parametrized random variables where the grounding is not polynomial in a population size, we present lifted inference which is polynomial in the population size whereas grounding is exponential. Neither of these cases requires reasoning separately about the individuals that are not explicitly mentioned.

Connected through hyperedge HE-1 (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-2 (induced by source paper "quantified markov logic networks"):
  with members:
    - "weighted model counting beyond two variable logic"
    - "entropy optimization and counting"

Connected through hyperedge HE-3 (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"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "lifted graphical models a survey"

Connected through hyperedge HE-4 (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"
    - "markov chains on orbits of permutation groups"
    - "probabilistic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "why is compiling lifted inference into a low level language so effective"):
  with members:
    - "relational bayesian networks"
    - "constraint processing in lifted probabilistic inference"
    - "probabilistic theorem proving"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-6 (induced by source paper "first order decomposition trees"):
  with members:
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"
    - "solving sat and bayesian inference with backtracking search"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-7 (induced by source paper "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"):
  with members:
    - "approximating the partition function by deleting and then correcting for model edges"
    - "bisimulation based approximate lifted inference"
    - "iterative join graph propagation"
    - "node splitting a scheme for generating upper bounds in bayesian networks"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-8 (induced by source paper "lifted variable elimination a novel operator and completeness results"):
  with members:
    - "bisimulation based approximate lifted inference"
    - "counting belief propagation"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"
    - "exploiting contextual independence in 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)