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: "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.

Connected through hyperedge HE-1 (induced by source paper "on tractable computation of expected predictions"):
  with members:
    - "visualizing and understanding sum product networks"
    - "smoothing structured decomposable circuits"
    - "what to expect of classifiers reasoning about logistic regression with missing features"
    - "learning logistic circuits"
    - "metabags bagged meta decision trees for regression"
    - "explaining image classifiers by counterfactual generation"
    - "a unified approach to interpreting model predictions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (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"
    - "an approximate nonmyopic computation for value of information"
    - "near optimal nonmyopic value of information in graphical models"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "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-3 (induced by source paper "efficient online learning for optimizing value of information theory and application to interactive troubleshooting"):
  with members:
    - "near optimal bayesian active learning for decision making"
    - "using robdds for inference in bayesian networks with troubleshooting as an example"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "near optimal edge evaluation in explicit generalized binomial graphs"):
  with members:
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "active learning amidst logical knowledge"):
  with members:
    - "estimating accuracy from unlabeled data a probabilistic logic approach"
    - "a graph theoretic analysis of information value"

Connected through hyperedge HE-6 (induced by source paper "adaptive submodularity theory and applications in active learning and stochastic optimization"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "near optimal nonmyopic value of information in graphical models"
    - "adaptive submodular optimization under matroid constraints"
    - "near optimal bayesian active learning with noisy observations"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "interactive submodular set cover"
    - "average case active learning with costs"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-7 (induced by source paper "a utility theoretic approach to privacy in online services"):
  with members:
    - "an approximate nonmyopic computation for value of information"
    - "near optimal nonmyopic value of information in graphical models"
    - "a learning theory approach to non interactive database privacy"
    - "active learning with statistical models"

Connected through hyperedge HE-8 (induced by source paper "near optimal bayesian active learning with noisy observations"):
  with members:
    - "efficient test selection in active diagnosis via entropy approximation"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"

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)