human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the sample complexity of learning bayesian networks"
  Abstract: In recent years there has been an increasing interest in learning Bayesian networks from data. One of the most effective methods for learning such networks is based on the minimum description length (MDL) principle. Previous work has shown that this learning procedure is asymptotically successful: with probability one, it will converge to the target distribution, given a sufficient number of samples. However, the rate of this convergence has been hitherto unknown. In this work we examine the sample complexity of MDL based learning procedures for Bayesian networks. We show that the number of samples needed to learn an epsilon-close approximation (in terms of entropy distance) with confidence delta is O((1/epsilon)^(4/3)log(1/epsilon)log(1/delta)loglog (1/delta)). This means that the sample complexity is a low-order polynomial in the error threshold and sub-linear in the confidence bound. We also discuss how the constants in this term depend on the complexity of the target distribution. Finally, we address questions of asymptotic minimality and propose a method for using the sample complexity results to speed up the learning process.

Connected through hyperedge HE-1 (induced by source paper "testing bayesian networks"):
  with members:
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "a size free clt for poisson multinomials and its applications"
    - "collision based testers are optimal for uniformity and closeness"
    - "optimal testing for properties of distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "sample optimal density estimation in nearly linear time"
    - "optimal algorithms and lower bounds for testing closeness of structured distributions"
    - "testing shape restrictions of discrete distributions"

Connected through hyperedge HE-2 (induced by source paper "information theoretic limits of bayesian network structure learning"):
  with members:
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "learning factor graphs in polynomial time sample complexity"
    - "finding optimal bayesian networks"
    - "on the number of samples needed to learn the correct structure of a bayesian network"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "information theoretic limits of learning the structure of bayesian networks"):
  with members:
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "finding optimal bayesian networks"
    - "on the number of samples needed to learn the correct structure of a bayesian network"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-4 (induced by source paper "incorporating type ii error probabilities from independence tests into score based learning of bayesian network structure"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "bayesian network learning with cutting planes"
    - "strong faithfulness and uniform consistency in causal inference"
    - "on the number of samples needed to learn the correct structure of a bayesian network"

Connected through hyperedge HE-5 (induced by source paper "learning bayesian networks with local structure"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "context specific independence in bayesian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "sparsityboost a new scoring function for learning bayesian network structure"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "bayesian network learning with cutting planes"
    - "on the number of samples needed to learn the correct structure of a bayesian network"

Connected through hyperedge HE-7 (induced by source paper "learning bayesian nets that perform well"):
  with members:
    - "a tutorial on learning with bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "learning factor graphs in polynomial time sample complexity"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "efficient stepwise selection in decomposable models"
    - "efficiently inducing features of conditional random fields"
    - "finding optimal bayesian networks"
    - "pac learning bounded tree width graphical models"
    - "finding a path is harder than finding a tree"

Question: Please tell me which class the center node 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)