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: "cached sufficient statistics for efficient machine learning with large datasets"
  Abstract: This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of non-zero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worst-case bounds for this structure for several models of data distribution. We empirically demonstrate that tractably-sized data structures can be produced for large real-world datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its leaves. We show how the ADtree can be used to accelerate Bayes net structure finding algorithms, rule learning algorithms, and feature selection algorithms, and we provide a number of empirical results comparing ADtree methods against traditional direct counting approaches. We also discuss the possible uses of ADtrees in other machine learning methods, and discuss the merits of ADtrees in comparison with alternative representations such as kd-trees, R-trees and Frequent Sets.

Connected through hyperedge HE-1 (induced by source paper "the use of machine learning and big five personality taxonomy to predict construction workers safety behaviour"):
  with members:
    - "an immersive virtual reality serious game to enhance earthquake behavioral responses and post earthquake evacuation preparedness in buildings"
    - "the effectiveness of traditional tools and computer aided technologies for health and safety training in the construction sector a systematic review"
    - "immersive virtual reality serious games for evacuation training and research a systematic literature review"
    - "linear system identification in a nonlinear setting nonparametric analysis of the nonlinear distortions and their impact on the best linear approximation"

Connected through hyperedge HE-2 (induced by source paper "fast learning of relational dependency networks"):
  with members:
    - "computing multi relational sufficient statistics for large databases"
    - "deep generative stochastic networks trainable by backprop"
    - "learning bayesian networks with local structure"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "discriminative probabilistic models for relational data"
    - "finding optimal bayesian networks"
    - "closed form learning of markov networks from dependency networks"
    - "lifted graphical models a survey"

Connected through hyperedge HE-3 (induced by source paper "the grow shrink strategy for learning markov network structures constrained by context specific independences"):
  with members:
    - "the ibmap approach for markov network structure learning"
    - "yggdrasil a statistical package for learning split models"
    - "learning markov networks with context specific independences"

Connected through hyperedge HE-4 (induced by source paper "learning bayesian network structure from massive datasets the sparse candidate algorithm"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "fraud uncollectible debt detection using a bayesian network based learning system a rare binary outcome with mixed data structures"

Connected through hyperedge HE-5 (induced by source paper "fast learning from sparse data"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "learning markov networks with context specific independences"):
  with members:
    - "markov random fields factorization with context specific independences"
    - "context specific independence in bayesian networks"
    - "inference for multiplicative models"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-7 (induced by source paper "bayesian structure learning using dynamic programming and mcmc"):
  with members:
    - "learning the structure of dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal discovery from a mixture of experimental and observational data"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "structured priors for structure learning"
    - "finding optimal bayesian networks"
    - "advances in exact bayesian structure discovery in bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "improving the scalability of optimal bayesian network learning with external memory frontier breadth first branch and bound search"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a branch and bound algorithm for mdl learning bayesian networks"
    - "exact structure discovery in bayesian networks with less space"
    - "a simple approach for finding the globally optimal bayesian network structure"

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.AI(Artificial Intelligence)