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: "ordering based search a simple and effective algorithm for learning bayesian networks"
  Abstract: One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NP-hard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu lists; moreover, many of the proposed algorithms are quite complex and hard to implement. In this paper, we propose a very simple and easy-to-implement method for addressing this task. Our approach is based on the well-known fact that the best network (of bounded in-degree) consistent with a given node ordering can be found very efficiently. We therefore propose a search not over the space of structures, but over the space of orderings, selecting for each ordering the best network consistent with it. This search space is much smaller, makes more global search steps, has a lower branching factor, and avoids costly acyclicity checks. We present results for this algorithm on both synthetic and real data sets, evaluating both the score of the network found and in the running time. We show that ordering-based search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement.

Connected through hyperedge HE-1 (induced by source paper "characterizing distribution equivalence for cyclic and acyclic directed graphs"):
  with members:
    - "a polynomial time algorithm for deciding markov equivalence of directed cyclic graphical models"
    - "a discovery algorithm for directed cyclis graphs"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "directed cyclic graphical representations of feedback models"
    - "on the testable implications of causal models with hidden variables"
    - "discovering cyclic causal models by independent components analysis"

Connected through hyperedge HE-2 (induced by source paper "solving all regression models for learning gaussian networks using givens rotations"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "finding optimal bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "finding all bayesian network structures within a factor of optimal"):
  with members:
    - "entropy based pruning for learning bayesian networks using bic"
    - "tractable bayesian learning of tree belief networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "advances in bayesian network learning using integer programming"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian model averaging using the k best bayesian network structures"

Connected through hyperedge HE-4 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "pac learning bounded tree width graphical models"
    - "multivariate information measures an experimentalist s perspective"

Connected through hyperedge HE-5 (induced by source paper "renormalized normalized maximum likelihood and three part code criteria for learning gaussian networks"):
  with members:
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-6 (induced by source paper "efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena"):
  with members:
    - "causal data science for financial stress testing"
    - "modeling cumulative biological phenomena with suppes bayes causal networks"
    - "efficient simulation of financial stress testing scenarios with suppes bayes causal networks"
    - "exposing the probabilistic causal structure of discrimination"
    - "parallel implementation of efficient search schemes for the inference of cancer progression models"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "on learning the structure of bayesian networks and submodular function maximization"):
  with members:
    - "data analysis with bayesian networks a bootstrap approach"
    - "learning gaussian networks"

Connected through hyperedge HE-8 (induced by source paper "entropy based pruning for learning bayesian networks using bic"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a branch and bound algorithm for mdl learning bayesian networks"

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)