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 "query the model precomputations for efficient inference with bayesian networks"):
  Abstract: We consider a setting where a Bayesian network has been built over a relational database to represent the joint distribution of data attributes and is used to perform approximate query processing or answer predictive queries. We explain how careful precomputation of certain probabilistic quantities can lead to large efficiency gains in response time and provide algorithms for optimal precomputation.

Includes members:
  Member "a simple cache model for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalization through memorization nearest neighbor language models")
  Member "generative neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards generating long and coherent text with multi level latent variable models")
  Member "database learning toward a database that becomes smarter every time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ml aqp query driven approximate query processing based on machine learning")
  Member "d separation from theorems to algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning bayesian networks from demographic and health survey data")
  Member "exploiting causal independence in bayesian network inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an advance on variable elimination with applications to tensor based computation")

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)