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: "finding a path is harder than finding a tree"
  Abstract: I consider the problem of learning an optimal path graphical model from data and show the problem to be NP-hard for the maximum likelihood and minimum description length approaches and a Bayesian approach. This hardness result holds despite the fact that the problem is a restriction of the polynomially solvable problem of finding the optimal tree graphical model.

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"
    - "on finding optimal polytrees"
    - "pac learning bounded tree width graphical models"

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"

Connected through hyperedge HE-3 (induced by source paper "learning factor graphs in polynomial time sample complexity"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "on the sample complexity of learning bayesian networks"
    - "efficient stepwise selection in decomposable models"
    - "efficiently inducing features of conditional random fields"
    - "finding optimal bayesian networks"
    - "pac learning bounded tree width graphical models"

Connected through hyperedge HE-4 (induced by source paper "large sample learning of bayesian networks is np hard"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "a transformational characterization of equivalent bayesian network structures"
    - "properties of bayesian belief network learning algorithms"
    - "on local optima in learning bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "finding optimal bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"

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)