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 "information theoretic limits of learning the structure of bayesian networks"):
  Abstract: In this paper, we study the information-theoretic limits of learning the structure of Bayesian networks, on discrete as well as continuous random variables, from a finite amount of data. We show that under certain parameterizations of the Bayesian network --- the minimum number of samples required to learn the "true" network grows as $\mathcal{O}(M)$ and $\mathcal{O}(S\log M)$ for non-sparse and sparse Bayesian networks respectively --- where $M$ is the number of variables in the network and $S$ is the maximum number of parents of any node in the network. We study various commonly used Bayesian networks, such as Conditional Probability Table (CPT) based networks, Noisy-OR networks, Logistic regression (LR) networks, and Gaussian networks. We identify various important parameters of the conditional distributions that affect the complexity of learning such models, like the maximum inverse probability for CPT networks, the failure probability for Noisy-OR networks, the $\ell_2$ norm of weight vectors for LR networks and the signal and noise parameters for Gaussian networks. We also show that an existing procedure calle SparsityBoost, by Brenner and Sontag, for learning binary CPT networks is information-theoretically optimal in the number of variables.

Includes members:
  Member "on the sample complexity of learning bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "testing bayesian networks")
  Member "sparsityboost a new scoring function for learning bayesian network structure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computationally and statistically efficient learning of causal bayes nets using path queries")
  Member "finding optimal bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "d vae a variational autoencoder for directed acyclic graphs")
  Member "on the number of samples needed to learn the correct structure of a bayesian network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "survey of bayesian networks applications on unmanned intelligent autonomous vehicles")
  Member "information theoretic limits of selecting binary graphical models in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privately learning markov random fields")

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)