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 bayesian network structure learning"):
  Abstract: In this paper, we study the information-theoretic limits of learning the structure of Bayesian networks (BNs), on discrete as well as continuous random variables, from a finite number of samples. We show that the minimum number of samples required by any procedure to recover the correct structure grows as $\Omega(m)$ and $\Omega(k \log m + (k^2/m))$ for non-sparse and sparse BNs respectively, where $m$ is the number of variables and $k$ is the maximum number of parents per node. We provide a simple recipe, based on an extension of the Fano's inequality, to obtain information-theoretic limits of structure recovery for any exponential family BN. We instantiate our result for specific conditional distributions in the exponential family to characterize the fundamental limits of learning various commonly used BNs, such as conditional probability table based networks, gaussian BNs, noisy-OR networks, and logistic regression networks. En route to obtaining our main results, we obtain tight bounds on the number of sparse and non-sparse essential-DAGs. Finally, as a byproduct, we recover the information-theoretic limits of sparse variable selection for logistic regression.

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 "learning factor graphs in polynomial time sample complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the number of samples needed to learn the correct structure of a bayesian network")
  Member "finding optimal bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (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)