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: "hybrid processing of beliefs and constraints"
  Abstract: This paper explores algorithms for processing probabilistic and deterministic information when the former is represented as a belief network and the latter as a set of boolean clauses. The motivating tasks are 1. evaluating beliefs networks having a large number of deterministic relationships and2. evaluating probabilities of complex boolean querie over a belief network. We propose a parameterized family of variable elimination algorithms that exploit both types of information, and that allows varying levels of constraint propagation inferences. The complexity of the scheme is controlled by the induced-width of the graph {em augmented} by the dependencies introduced by the boolean constraints. Preliminary empirical evaluation demonstrate the effect of constraint propagation on probabilistic computation.

Connected through hyperedge HE-1 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-2 (induced by source paper "revisiting algebra and complexity of inference in graphical models"):
  with members:
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "survey propagation revisited"
    - "robust max product belief propagation"
    - "approximating the permanent with belief propagation"
    - "an algebraic graphical model for decision with uncertainties feasibilities and utilities"
    - "complexity results and approximation strategies for map explanations"
    - "a new look at survey propagation and its generalizations"

Connected through hyperedge HE-3 (induced by source paper "algebra of inference in graphical models revisited"):
  with members:
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "robust max product belief propagation"
    - "approximating the permanent with belief propagation"
    - "complexity results and approximation strategies for map explanations"
    - "a new look at survey propagation and its generalizations"

Connected through hyperedge HE-4 (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"
    - "on triangulating dynamic graphical models"
    - "value elimination bayesian inference via backtracking search"
    - "convolutional factor graphs as probabilistic models"

Connected through hyperedge HE-5 (induced by source paper "modeling transportation routines using hybrid dynamic mixed networks"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "iterative join graph propagation"
    - "on finding minimal w cutset"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "a simple insight into iterative belief propagation s success"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "new advances in inference by recursive conditioning"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"

Connected through hyperedge HE-7 (induced by source paper "mixtures of deterministic probabilistic networks and their and or search space"):
  with members:
    - "new advances in inference by recursive conditioning"

Connected through hyperedge HE-8 (induced by source paper "an algebraic graphical model for decision with uncertainties feasibilities and utilities"):
  with members:
    - "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
    - "qualitative models for decision under uncertainty without the commensurability assumption"
    - "an order of magnitude calculus"
    - "graphical models for preference and utility"
    - "plausibility measures a user s guide"
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"
    - "context specific independence in bayesian networks"
    - "a general non probabilistic theory of inductive reasoning"

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)