human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "survey of bayesian networks applications on unmanned intelligent autonomous vehicles"):
  Abstract: This article review the applications of Bayesian networks on Unmanned Intelligent Autonomous Vehicles (UIAV) from the decision making point of view, which represents the final step for fully autonomous unmanned vehicles (currently under discussion). Until now when it comes to make high level decisions for unmanned autonomous vehicles (UAV) the humans have the last word. Based on the works exposed in this article and current analysis, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian networks as a promising computer model for decision making. Remains for the future to test Bayesian networks models in real situations. Besides the applications, Bayesian networks fundaments are introduced as elements to consider when we try to develop (UIAVs) with the potential of achieving high level judgements.

Includes members:
  Member "a tutorial on learning with bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a content based deep intrusion detection system")
  Member "infrastructure enabled autonomy a distributed intelligence architecture for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "survey of bayesian networks applications to intelligent autonomous vehicles")
  Member "on the number of samples needed to learn the correct structure of a bayesian network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information theoretic limits of bayesian network structure learning")

Question: Please tell me which class the source paper 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)