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: "exploiting contextual independence in probabilistic inference"
  Abstract: Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present such a representation that exploits contextual independence in terms of parent contexts; which variables act as parents may depend on the value of other variables. The internal representation is in terms of contextual factors (confactors) that is simply a pair of a context and a table. The algorithm, contextual variable elimination, is based on the standard variable elimination algorithm that eliminates the nonquery variables in turn, but when eliminating a variable, the tables that need to be multiplied can depend on the context. This algorithm reduces to standard variable elimination when there is no contextual independence structure to exploit. We show how this can be much more efficient than variable elimination when there is structure to exploit. We explain why this new method can exploit more structure than previous methods for structured belief network inference and an analogous algorithm that uses trees.

Connected through hyperedge HE-1 (induced by source paper "addmc exact weighted model counting with algebraic decision diagrams"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "solving sat and bayesian inference with backtracking search"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-2 (induced by source paper "from high level inference algorithms to efficient code"):
  with members:
    - "autoconj recognizing and exploiting conjugacy without a domain specific language"
    - "exact inference for relational graphical models with interpreted functions lifted probabilistic inference modulo theories"
    - "swift compiled inference for probabilistic programming languages"
    - "probabilistic inference modulo theories"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "dynamic blocking and collapsing for gibbs sampling"
    - "church a language for generative models"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-3 (induced by source paper "fusing first order knowledge compilation and the lifted junction tree algorithm"):
  with members:
    - "why is compiling lifted inference into a low level language so effective"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted variable elimination for probabilistic logic programming"
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "lifted inference for relational continuous models"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"
    - "a knowledge compilation map"

Connected through hyperedge HE-4 (induced by source paper "hypertree decompositions revisited for pgms"):
  with members:
    - "size bounds and query plans for relational joins"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "skew strikes back new developments in the theory of join algorithms"
    - "join graph propagation algorithms"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "causal independence for knowledge acquisition and inference"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"

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"
    - "towards completely lifted search based probabilistic inference"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-7 (induced by source paper "markov random fields factorization with context specific independences"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "context specific independence in bayesian networks"
    - "inference for multiplicative models"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-8 (induced by source paper "learning markov networks with context specific independences"):
  with members:
    - "markov random fields factorization with context specific independences"
    - "context specific independence in bayesian networks"
    - "inference for multiplicative models"
    - "cached sufficient statistics for efficient machine learning with large datasets"

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)