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 "optimal value of information in graphical models"):
  Abstract: Many real-world decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. In medical decision making tasks, one needs to select which tests to administer before deciding on the most effective treatment. It has been general practice to use heuristic-guided procedures for selecting observations. In this paper, we present the first efficient optimal algorithms for selecting observations for a class of probabilistic graphical models. For example, our algorithms allow to optimally label hidden variables in Hidden Markov Models (HMMs). We provide results for both selecting the optimal subset of observations, and for obtaining an optimal conditional observation plan.#R##N##R##N#Furthermore we prove a surprising result: In most graphical models tasks, if one designs an efficient algorithm for chain graphs, such as HMMs, this procedure can be generalized to poly-tree graphical models. We prove that the optimizing value of information is NPPP-hard even for polytrees. It also follows from our results that just computing decision theoretic value of information objective functions, which are commonly used in practice, is a #P-complete problem even on Naive Bayes models (a simple special case of polytrees).#R##N##R##N#In addition, we consider several extensions, such as using our algorithms for scheduling observation selection for multiple sensors. We demonstrate the effectiveness of our approach on several real-world datasets, including a prototype sensor network deployment for energy conservation in buildings.

Includes members:
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "inference in hybrid networks theoretical limits and practical algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient online learning for optimizing value of information theory and application to interactive troubleshooting")
  Member "an approximate nonmyopic computation for value of information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "value of information in probabilistic logic programs")
  Member "learning diagnostic policies from examples by systematic search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near optimal nonmyopic value of information in graphical models")
  Member "near optimal nonmyopic value of information in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "complexity results and approximation strategies for map explanations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate mmap by marginal search")
  Member "set k cover algorithms for energy efficient monitoring in wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scheduling resource bounded monitoring devices for event detection and isolation in networks")
  Member "the computational complexity of probabilistic planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a rate distortion framework for explaining neural network decisions")

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)