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 "fast learning of relational dependency networks"):
  Abstract: A Relational Dependency Network (RDN) is a directed graphical model widely used for multi-relational data. These networks allow cyclic dependencies, necessary to represent relational autocorrelations. We describe an approach for learning both the RDN's structure and its parameters, given an input relational database: First learn a Bayesian network (BN), then transform the Bayesian network to an RDN. Thus fast Bayes net learning can provide fast RDN learning. The BN-to-RDN transform comprises a simple, local adjustment of the Bayes net structure and a closed-form transform of the Bayes net parameters. This method can learn an RDN for a dataset with a million tuples in minutes. We empirically compare our approach to state-of-the art RDN learning methods that use functional gradient boosting, on five benchmark datasets. Learning RDNs via BNs scales much better to large datasets than learning RDNs with boosting, and provides competitive accuracy in predictions.

Includes members:
  Member "computing multi relational sufficient statistics for large databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sql for srl structure learning inside a database system")
  Member "deep generative stochastic networks trainable by backprop":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders")
  Member "learning bayesian networks with local structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms")
  Member "learning bayesian network structure from massive datasets the sparse candidate algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample debiasing in the themis open world database system extended version")
  Member "discriminative probabilistic models for relational data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning distributional programs for relational autocompletion")
  Member "finding optimal bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "d vae a variational autoencoder for directed acyclic graphs")
  Member "closed form learning of markov networks from dependency networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the libra toolkit for probabilistic models")
  Member "lifted graphical models a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")

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.LG(Machine Learning)