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: "the recovery of causal poly trees from statistical data"
  Abstract: Poly-trees are singly connected causal networks in which variables may arise from multiple causes. This paper develops a method of recovering ply-trees from empirically measured probability distributions of pairs of variables. The method guarantees that, if the measured distributions are generated by a causal process structured as a ply-tree then the topological structure of such tree can be recovered precisely and, in addition, the causal directionality of the branches can be determined up to the maximum extent possible. The method also pinpoints the minimum (if any) external semantics required to determine the causal relationships among the variables considered.

Connected through hyperedge HE-1 (induced by source paper "dempster shafer belief function a new interpretation"):
  with members:
    - "a new approach to updating beliefs"

Connected through hyperedge HE-2 (induced by source paper "evidence against evidence theory"):
  with members:
    - "a new approach to updating beliefs"

Connected through hyperedge HE-3 (induced by source paper "p causality identifying spatiotemporal causal pathways for air pollutants with urban big data"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-4 (induced by source paper "query expansion in information retrieval systems using a bayesian network based thesaurus"):
  with members:
    - "an architecture for probabilistic concept based information retrieval"

Connected through hyperedge HE-5 (induced by source paper "an entropy based learning algorithm of bayesian conditional trees"):
  with members:
    - "advances in probabilistic reasoning"

Connected through hyperedge HE-6 (induced by source paper "using causal information and local measures to learn bayesian networks"):
  with members:
    - "a bayesian method for constructing bayesian belief networks from databases"

Connected through hyperedge HE-7 (induced by source paper "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"):
  with members:
    - "an algorithm for the construction of bayesian network structures from data"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks with local structure"
    - "causal inference and causal explanation with background knowledge"
    - "a transformational characterization of equivalent bayesian network structures"
    - "causal networks semantics and expressiveness"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "improved learning of 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.AI(Artificial Intelligence)