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: "a simple approach for finding the globally optimal bayesian network structure"
  Abstract: We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC. This problem is known to be NP-hard, which means that solving it becomes quickly infeasible as the number of variables increases. Nevertheless, in this paper we show that it is possible to learn the best Bayesian network structure with over 30 variables, which covers many practically interesting cases. Our algorithm is less complicated and more efficient than the techniques presented earlier. It can be easily parallelized, and offers a possibility for efficient exploration of the best networks consistent with different variable orderings. In the experimental part of the paper we compare the performance of the algorithm to the previous state-of-the-art algorithm. Free source-code and an online-demo can be found at this http URL

Connected through hyperedge HE-1 (induced by source paper "dag gnn dag structure learning with graph neural networks"):
  with members:
    - "neural message passing for quantum chemistry"
    - "joint causal inference from multiple contexts"
    - "gated graph sequence neural networks"
    - "adam a method for stochastic optimization"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "data analysis with bayesian networks a bootstrap approach"
    - "spectral networks and locally connected networks on graphs"
    - "causal inference in the presence of latent variables and selection bias"

Connected through hyperedge HE-2 (induced by source paper "integer programming for learning directed acyclic graphs from continuous data"):
  with members:
    - "information theoretic limits of bayesian network structure learning"
    - "bayesian network structure learning with integer programming polytopes facets and complexity"
    - "advances in bayesian network learning using integer programming"
    - "bayesian network learning with cutting planes"
    - "exact structure discovery in bayesian networks with less space"
    - "advances in exact bayesian structure discovery in bayesian networks"

Connected through hyperedge HE-3 (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"
    - "finding optimal bayesian networks"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "learning bayesian networks with low rank conditional probability tables"):
  with members:
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "bayesian network learning by compiling to weighted max sat"

Connected through hyperedge HE-5 (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"
    - "bayesian model averaging using the k best bayesian network structures"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "learning binary bayesian networks in polynomial time and sample complexity"):
  with members:
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "bayesian network learning by compiling to weighted max sat"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-7 (induced by source paper "renormalized normalized maximum likelihood and three part code criteria for learning gaussian networks"):
  with members:
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "survey on models and techniques for root cause analysis"):
  with members:
    - "learning possibilistic logic theories from default rules"
    - "learning tractable probabilistic models for fault localization"
    - "using abduction in markov logic networks for root cause analysis"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "evaluating computational models of explanation using human judgments"

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)