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 "efficient online learning for optimizing value of information theory and application to interactive troubleshooting"):
  Abstract: We consider the optimal value of information (VoI) problem, where the goal is to sequentially select a set of tests with a minimal cost, so that one can efficiently make the best decision based on the observed outcomes. Existing algorithms are either heuristics with no guarantees, or scale poorly (with exponential run time in terms of the number of available tests). Moreover, these methods assume a known distribution over the test outcomes, which is often not the case in practice. We propose an efficient sampling-based online learning framework to address the above issues. First, assuming the distribution over hypotheses is known, we propose a dynamic hypothesis enumeration strategy, which allows efficient information gathering with strong theoretical guarantees. We show that with sufficient amount of samples, one can identify a near-optimal decision with high probability. Second, when the parameters of the hypotheses distribution are unknown, we propose an algorithm which learns the parameters progressively via posterior sampling in an online fashion. We further establish a rigorous bound on the expected regret. We demonstrate the effectiveness of our approach on a real-world interactive troubleshooting application and show that one can efficiently make high-quality decisions with low cost.

Includes members:
  Member "near optimal bayesian active learning for decision making":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptivity in adaptive submodularity")
  Member "using robdds for inference in bayesian networks with troubleshooting as an example":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted positive binary decision diagrams for exact probabilistic inference")
  Member "inference in hybrid networks theoretical limits and practical algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exact inference in networks with discrete children of continuous parents")
  Member "analysis of thompson sampling for the multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "near optimal bayesian active learning with noisy observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active learning in the overparameterized and interpolating regime")
  Member "adaptive submodularity theory and applications in active learning and stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "comprehensive and efficient data labeling via adaptive model scheduling")
  Member "optimal value of information in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on tractable computation of expected predictions")

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)