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 number of samples needed to learn the correct structure of a bayesian network"
  Abstract: Bayesian Networks (BNs) are useful tools giving a natural and compact representation of joint probability distributions. In many applications one needs to learn a Bayesian Network (BN) from data. In this context, it is important to understand the number of samples needed in order to guarantee a successful learning. Previous work have studied BNs sample complexity, yet it mainly focused on the requirement that the learned distribution will be close to the original distribution which generated the data. In this work, we study a different aspect of the learning, namely the number of samples needed in order to learn the correct structure of the network. We give both asymptotic results, valid in the large sample limit, and experimental results, demonstrating the learning behavior for feasible sample sizes. We show that structure learning is a more difficult task, compared to approximating the correct distribution, in the sense that it requires a much larger number of samples, regardless of the computational power available for the learner.

Connected through hyperedge HE-1 (induced by source paper "survey of bayesian networks applications on unmanned intelligent autonomous vehicles"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "infrastructure enabled autonomy a distributed intelligence architecture for autonomous vehicles"

Connected through hyperedge HE-2 (induced by source paper "information theoretic limits of bayesian network structure learning"):
  with members:
    - "on the sample complexity of learning bayesian networks"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "learning factor graphs in polynomial time sample complexity"
    - "finding optimal bayesian networks"
    - "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:
    - "on the sample complexity of learning bayesian networks"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "finding optimal bayesian networks"
    - "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"
    - "on the sample complexity of learning bayesian networks"
    - "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"

Connected through hyperedge HE-5 (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"
    - "on the sample complexity of learning bayesian networks"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "bayesian network learning with cutting planes"

Connected through hyperedge HE-6 (induced by source paper "learning high dimensional markov forest distributions analysis of error rates"):
  with members:
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "reconstruction of markov random fields from samples some easy observations and algorithms"

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)