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: "a method for implementing a probabilistic model as a relational database"
  Abstract: This paper discusses a method for implementing a probabilistic inference system based on an extended relational data model. This model provides a unified approach for a variety of applications such as dynamic programming, solving sparse linear equations, and constraint propagation. In this framework, the probability model is represented as a generalized relational database. Subsequent probabilistic requests can be processed as standard relational queries. Conventional database management systems can be easily adopted for implementing such an approximate reasoning system.

Connected through hyperedge HE-1 (induced by source paper "are key foreign key joins safe to avoid when learning high capacity classifiers"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "visual search at pinterest"
    - "adam a method for stochastic optimization"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "graphlab a new framework for parallel machine learning"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-2 (induced by source paper "stop that join discarding dimension tables when learning high capacity classifiers"):
  with members:
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "factorbase sql for learning a multi relational graphical model"):
  with members:
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "lifted graphical models a survey"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "scalable probabilistic databases with factor graphs and mcmc"

Connected through hyperedge HE-4 (induced by source paper "loglinear models for first order probabilistic reasoning"):
  with members:
    - "context specific independence in bayesian networks"
    - "learning mixtures of dag models"
    - "relational bayesian networks"

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)