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: "an approximate nonmyopic computation for value of information"
  Abstract: Value-of-information analyses provide a straightforward means for selecting the best next observation to make, and for determining whether it is better to gather additional information or to act immediately. Determining the next best test to perform, given a state of uncertainty about the world, requires a consideration of the value of making all possible sequences of observations. In practice, decision analysts and expert-system designers have avoided the intractability of exact computation of the value of information by relying on a myopic approximation. Myopic analyses are based on the assumption that only one additional test will be performed, even when there is an opportunity to make a large number of observations. We present a nonmyopic approximation for value of information that bypasses the traditional myopic analyses by exploiting the statistical properties of large samples.

Connected through hyperedge HE-1 (induced by source paper "value of information in probabilistic logic programs"):
  with members:
    - "an anytime algorithm for decision making under uncertainty"
    - "myopic value of information in influence diagrams"
    - "near optimal nonmyopic value of information in graphical models"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "optimal value of information in graphical models"
    - "reasoning and planning with sensing actions incomplete information and static causal laws using answer set programming"
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

Connected through hyperedge HE-2 (induced by source paper "a graph theoretic analysis of information value"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"
    - "reformulating inference problems through selective conditioning"
    - "efficient decision theoretic planning techniques and empirical analysis"
    - "ideal reformulation of belief networks"
    - "dynamic construction of belief networks"
    - "chain graphs for learning"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "representing context sensitive knowledge in a network formalism a preliminary report"

Connected through hyperedge HE-3 (induced by source paper "normative engineering risk management systems"):
  with members:
    - "time dependent utility and action under uncertainty"
    - "decision making using probabilistic inference methods"
    - "r d analyst an interactive approach to normative decision system model construction"

Connected through hyperedge HE-4 (induced by source paper "pay or play"):
  with members:
    - "a graph theoretic analysis of information value"

Connected through hyperedge HE-5 (induced by source paper "efficient test selection in active diagnosis via entropy approximation"):
  with members:
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-6 (induced by source paper "learning diagnostic policies from examples by systematic search"):
  with members:
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

Connected through hyperedge HE-7 (induced by source paper "near optimal nonmyopic value of information in graphical models"):
  with members:
    - "learning diagnostic policies from examples by systematic search"

Connected through hyperedge HE-8 (induced by source paper "value of information lattice exploiting probabilistic independence for effective feature subset acquisition"):
  with members:
    - "learning diagnostic policies from examples by systematic search"
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

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)