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: "constraint processing in lifted probabilistic inference"
  Abstract: First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitations. Our theoretical results show that the wrong choice of constraint processing method can lead to exponential increase in computational complexity. Our empirical tests confirm the importance of constraint processing in lifted inference. This is the first theoretical and empirical study of constraint processing in lifted inference.

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 "why is compiling lifted inference into a low level language so effective"):
  with members:
    - "relational bayesian networks"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-3 (induced by source paper "lifted variable elimination for probabilistic logic programming"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "skolemization for weighted first order model counting"
    - "multiplicative factorization of noisy max"
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "lifted variable elimination decoupling the operators from the constraint language"):
  with members:
    - "bisimulation based approximate lifted inference"
    - "lifted inference for relational continuous models"
    - "counting belief propagation"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "probabilistic theorem proving"):
  with members:
    - "formula based probabilistic inference"
    - "bisimulation based approximate lifted inference"
    - "counting belief propagation"

Connected through hyperedge HE-6 (induced by source paper "lifted graphical models a survey"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "object oriented bayesian networks"
    - "discriminative probabilistic models for relational data"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-7 (induced by source paper "towards completely lifted search based probabilistic inference"):
  with members:
    - "extending factor graphs so as to unify directed and undirected graphical models"
    - "counting belief propagation"
    - "solving sat and bayesian inference with backtracking search"

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)