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: "the factored frontier algorithm for approximate inference in dbns"
  Abstract: The Factored Frontier (FF) algorithm is a simple approximate inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead of doing an exact update at every step followed bymarginalisation (projection), it always works with factoreddistributions. Hence it can be applied to models for which the exactupdate step is intractable. We show that FF is equivalent to (oneiteration of) loopy belief propagation (LBP) on the original DBN, andthat BK is equivalent (to one iteration of) LBP on a DBN where wecluster some of the nodes. We then show empirically that byiterating, LBP can improve on the accuracy of both FF and BK. Wecompare these algorithms on two real-world DBNs: the first is a modelof a water treatment plant, and the second is a coupled HMM, used tomodel freeway traffic.

Connected through hyperedge HE-1 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "belief and truth in hypothesised behaviours"
    - "exploiting causality for selective belief filtering in dynamic bayesian networks"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "tractable inference for complex stochastic processes"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-2 (induced by source paper "factored probabilistic belief tracking"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "tractable inference for complex stochastic processes"
    - "context specific independence in bayesian networks"
    - "a general non probabilistic theory of inductive reasoning"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "a simple insight into iterative belief propagation s success"

Connected through hyperedge HE-3 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks extended abstract"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "factored probabilistic belief tracking"
    - "tractable inference for complex stochastic processes"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "answering hindsight queries with lifted dynamic junction trees"):
  with members:
    - "exact inference techniques for the analysis of bayesian attack graphs"
    - "first order decomposition trees"
    - "lifted variable elimination decoupling the operators from the constraint language"

Connected through hyperedge HE-5 (induced by source paper "preventing unnecessary groundings in the lifted dynamic junction tree algorithm"):
  with members:
    - "first order decomposition trees"
    - "lifted variable elimination decoupling the operators from the constraint language"

Connected through hyperedge HE-6 (induced by source paper "amidst a java toolbox for scalable probabilistic machine learning"):
  with members:
    - "probabilistic graphical models on multi core cpus using java 8"
    - "operations for learning with graphical models"

Connected through hyperedge HE-7 (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"
    - "value directed belief state approximation for pomdps"

Connected through hyperedge HE-8 (induced by source paper "scaling pomdps for selecting sellers in e markets extended version"):
  with members:
    - "communication based decomposition mechanisms for decentralized mdps"

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)