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: "sufficiency separability and temporal probabilistic models"
  Abstract: Suppose we are given the conditional probability of one variable given some other variables.Normally the full joint distribution over the conditioning variablesis required to determine the probability of the conditioned variable.Under what circumstances are the marginal distributions over the conditioning variables sufficient to determine the probability ofthe conditioned variable?Sufficiency in this sense is equivalent to additive separability ofthe conditional probability distribution.Such separability structure is natural and can be exploited forefficient inference.Separability has a natural generalization to conditional separability.Separability provides a precise notion of weaklyinteracting subsystems in temporal probabilistic models.Given a system that is decomposed into separable subsystems, exactmarginal probabilities over subsystems at future points in time can becomputed by propagating marginal subsystem probabilities, rather thancomplete system joint probabilities.Thus, separability can make exact prediction tractable.However, observations can break separability,so exact monitoring of dynamic systems remains hard.

Connected through hyperedge HE-1 (induced by source paper "mixture of parents maximum entropy markov models"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "piecewise training for undirected models"

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)