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 graph theoretic analysis of information value"
  Abstract: We derive qualitative relationships about the informational relevance of variables in graphical decision models based on a consideration of the topology of the models. Specifically, we identify dominance relations for the expected value of information on chance variables in terms of their position and relationships in influence diagrams. The qualitative relationships can be harnessed to generate nonnumerical procedures for ordering uncertain variables in a decision model by their informational relevance.

Connected through hyperedge HE-1 (induced by source paper "active learning amidst logical knowledge"):
  with members:
    - "estimating accuracy from unlabeled data a probabilistic logic approach"
    - "optimal value of information in graphical models"

Connected through hyperedge HE-2 (induced by source paper "structured arc reversal and simulation of dynamic probabilistic networks"):
  with members:
    - "sensor validation using dynamic belief networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "myopic value of information in influence diagrams"):
  with members:
    - "value of evidence on influence diagrams"
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "using potential influence diagrams for probabilistic inference and decision making"

Connected through hyperedge HE-4 (induced by source paper "pay or play"):
  with members:
    - "an approximate nonmyopic computation for value of information"

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)