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 finding optimal polytrees"
  Abstract: Inferring probabilistic networks from data is a notoriously difficult task. Under various goodness-of-fit measures, finding an optimal network is NP-hard, even if restricted to polytrees of bounded in-degree. Polynomial-time algorithms are known only for rare special cases, perhaps most notably for branchings, that is, polytrees in which the in-degree of every node is at most one. Here, we study the complexity of finding an optimal polytree that can be turned into a branching by deleting some number of arcs or nodes, treated as a parameter. #R##N#We show that the problem can be solved via a matroid intersection formulation in polynomial time if the number of deleted arcs is bounded by a constant. The order of the polynomial time bound depends on this constant, hence the algorithm does not establish fixed-parameter tractability when parameterized by the number of deleted arcs. We show that a restricted version of the problem allows fixed-parameter tractability and hence scales well with the parameter. We contrast this positive result by showing that if we parameterize by the number of deleted nodes, a somewhat more powerful parameter, the problem is not fixed-parameter tractable, subject to a complexity-theoretic assumption.

Connected through hyperedge HE-1 (induced by source paper "selective greedy equivalence search finding optimal bayesian networks using a polynomial number of score evaluations"):
  with members:
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "large sample learning of bayesian networks is np hard"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "finding optimal bayesian networks"
    - "pac learning bounded tree width graphical models"
    - "finding a path is harder than finding a tree"

Connected through hyperedge HE-2 (induced by source paper "parameterized complexity results for exact bayesian network structure learning"):
  with members:
    - "on the hardness of losing weight"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a complete anytime algorithm for treewidth"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "algorithms and complexity results for exact bayesian structure learning"
    - "don t be strict in local search"

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)