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: "learning diagnostic policies from examples by systematic search"
  Abstract: A diagnostic policy specifies what test to perform next, based on the results of previous tests, and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process (MDP). We investigate two types of algorithms for solving this MDP: systematic search based on AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples, but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed to control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions: it discusses the factors that make systematic search feasible for diagnosis, and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods.

Connected through hyperedge HE-1 (induced by source paper "near optimal nonmyopic value of information in graphical models"):
  with members:
    - "an approximate nonmyopic computation for value of information"

Connected through hyperedge HE-2 (induced by source paper "value of information lattice exploiting probabilistic independence for effective feature subset acquisition"):
  with members:
    - "an approximate nonmyopic computation for value of information"
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

Connected through hyperedge HE-3 (induced by source paper "optimal value of information in graphical models"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "an approximate nonmyopic computation for value of information"
    - "near optimal nonmyopic value of information in graphical models"
    - "complexity results and approximation strategies for map explanations"
    - "set k cover algorithms for energy efficient monitoring in wireless sensor networks"
    - "the computational complexity of probabilistic planning"
    - "active learning with statistical models"

Connected through hyperedge HE-4 (induced by source paper "integrating learning from examples into the search for diagnostic policies"):
  with members:
    - "solving pomdps by searching in policy space"
    - "an approximate nonmyopic computation for value of information"
    - "types of cost in inductive concept learning"
    - "robust classification for imprecise environments"
    - "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)