human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "factorbase sql for learning a multi relational graphical model"):
  Abstract: We describe FactorBase, a new SQL-based framework that leverages a relational database management system to support multi-relational model discovery. A multi-relational statistical model provides an integrated analysis of the heterogeneous and interdependent data resources in the database. We adopt the BayesStore design philosophy: statistical models are stored and managed as first-class citizens inside a database. Whereas previous systems like BayesStore support multi-relational inference, FactorBase supports multi-relational learning. A case study on six benchmark databases evaluates how our system supports a challenging machine learning application, namely learning a first-order Bayesian network model for an entire database. Model learning in this setting has to examine a large number of potential statistical associations across data tables. Our implementation shows how the SQL constructs in FactorBase facilitate the fast, modular, and reliable development of highly scalable model learning systems.

Includes members:
  Member "a method for implementing a probabilistic model as a relational database":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "are key foreign key joins safe to avoid when learning high capacity classifiers")
  Member "the madlib analytics library or mad skills the sql":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "towards a unified architecture for in rdbms analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "lifted graphical models a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")
  Member "tuffy scaling up statistical inference in markov logic networks using an rdbms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "scalable probabilistic databases with factor graphs and mcmc":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint reasoning for multi faceted commonsense knowledge")

Question: Please tell me which class the source paper 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.DB(Databases)