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: "node splitting a scheme for generating upper bounds in bayesian networks"
  Abstract: We formulate in this paper the mini-bucket algorithm for approximate inference in terms of exact inference on an approximate model produced by splitting nodes in a Bayesian network. The new formulation leads to a number of theoretical and practical implications. First, we show that branchand- bound search algorithms that use minibucket bounds may operate in a drastically reduced search space. Second, we show that the proposed formulation inspires new minibucket heuristics and allows us to analyze existing heuristics from a new perspective. Finally, we show that this new formulation allows mini-bucket approximations to benefit from recent advances in exact inference, allowing one to significantly increase the reach of these approximations.

Connected through hyperedge HE-1 (induced by source paper "oblivious bounds on the probability of boolean functions"):
  with members:
    - "dissociation and propagation for approximate lifted inference with standard relational database management systems"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "structured message passing"
    - "approximation by quantization"
    - "formula based probabilistic inference"
    - "optimal upper and lower bounds for boolean expressions by dissociation"
    - "a knowledge compilation map"

Connected through hyperedge HE-2 (induced by source paper "dissociation and propagation for approximate lifted inference with standard relational database management systems"):
  with members:
    - "approximate lifted inference with probabilistic databases"
    - "linearized and single pass belief propagation"
    - "oblivious bounds on the probability of boolean functions"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "skolemization for weighted first order model counting"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic databases with markoviews"
    - "formula based probabilistic inference"

Connected through hyperedge HE-3 (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"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "sequential diagnosis by abstraction"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "systematic vs non systematic algorithms for solving the mpe task"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "join graph propagation algorithms"):
  with members:
    - "a scheme for approximating probabilistic inference"
    - "expectation propagation for approximate bayesian inference"
    - "value elimination bayesian inference via backtracking search"
    - "iterative join graph propagation"
    - "an importance sampling algorithm based on evidence pre propagation"
    - "a variational approach for approximating bayesian networks by edge deletion"
    - "structured region graphs morphing ep into gbp"
    - "a simple insight into iterative belief propagation s success"

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)