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: "value elimination bayesian inference via backtracking search"
  Abstract: Backtracking search is a powerful algorithmic paradigm that can be used to solve many problems. It is in a certain sense the dual of variable elimination; but on many problems, e.g., SAT, it is vastly superior to variable elimination in practice. Motivated by this we investigate the application of backtracking search to the problem of Bayesian inference (Bayes). We show that natural generalizations of known techniques allow backtracking search to achieve performance guarantees similar to standard algorithms for Bayes, and that there exist problems on which backtracking can in fact do much better. We also demonstrate that these ideas can be applied to implement a Bayesian inference engine whose performance is competitive with standard algorithms. Since backtracking search can very naturally take advantage of context specific structure, the potential exists for performance superior to standard algorithms on many problems.

Connected through hyperedge HE-1 (induced by source paper "the sum product theorem a foundation for learning tractable models"):
  with members:
    - "recursive decomposition for nonconvex optimization"
    - "a differential approach to inference in bayesian networks"
    - "algebraic model counting"
    - "sum product networks a new deep architecture"
    - "the relationship between and or search and variable elimination"
    - "complexity of inference in graphical models"
    - "solving sat and bayesian inference with backtracking search"
    - "the language of search"

Connected through hyperedge HE-2 (induced by source paper "non minimal triangulations for mixed stochastic deterministic graphical models"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "optimal decomposition of belief networks"
    - "optimal junction trees"
    - "pre processing for triangulation of probabilistic networks"
    - "bucket elimination a unifying framework for several probabilistic inference"
    - "hybrid processing of beliefs and constraints"
    - "on triangulating dynamic graphical models"
    - "convolutional factor graphs as probabilistic models"

Connected through hyperedge HE-3 (induced by source paper "case factor diagrams for structured probabilistic modeling"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "new advances in inference by recursive conditioning"

Connected through hyperedge HE-4 (induced by source paper "formula based probabilistic inference"):
  with members:
    - "context specific independence in bayesian networks"
    - "and or importance sampling"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"

Connected through hyperedge HE-5 (induced by source paper "the relationship between and or search and variable elimination"):
  with members:
    - "mixtures of deterministic probabilistic networks and their and or search space"

Connected through hyperedge HE-6 (induced by source paper "join graph propagation algorithms"):
  with members:
    - "a scheme for approximating probabilistic inference"
    - "expectation propagation for approximate bayesian inference"
    - "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"
    - "node splitting a scheme for generating upper bounds in bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "and or multi valued decision diagrams aomdds for graphical models"):
  with members:
    - "new advances in inference by recursive conditioning"
    - "case factor diagrams for structured probabilistic modeling"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "the relationship between and or search and variable elimination"
    - "and or multi valued decision diagrams aomdds for weighted graphical models"
    - "a knowledge compilation map"

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)