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: "efficient test selection in active diagnosis via entropy approximation"
  Abstract: We consider the problem of diagnosing faults in a system represented by a Bayesian network, where diagnosis corresponds to recovering the most likely state of unobserved nodes given the outcomes of tests (observed nodes). Finding an optimal subset of tests in this setting is intractable in general. We show that it is difficult even to compute the next most-informative test using greedy test selection, as it involves several entropy terms whose exact computation is intractable. We propose an approximate approach that utilizes the loopy belief propagation infrastructure to simultaneously compute approximations of marginal and conditional entropies on multiple subsets of nodes. We apply our method to fault diagnosis in computer networks, and show the algorithm to be very effective on realistic Internet-like topologies. We also provide theoretical justification for the greedy test selection approach, along with some performance guarantees.

Connected through hyperedge HE-1 (induced by source paper "asking easy questions a user friendly approach to active reward learning"):
  with members:
    - "learning an urban air mobility encounter model from expert preferences"
    - "batch active learning using determinantal point processes"
    - "learning from richer human guidance augmenting comparison based learning with feature queries"
    - "batch active preference based learning of reward functions"
    - "april active preference learning based reinforcement learning"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-2 (induced by source paper "a tight analysis of greedy yields subexponential time approximation for uniform decision tree"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "inapproximability of vc dimension and littlestone s dimension"
    - "inapproximability of nash equilibrium"
    - "noisy bayesian active learning"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"

Connected through hyperedge HE-3 (induced by source paper "generalized binary search for split neighborly problems"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "comparison based learning with rank nets"

Connected through hyperedge HE-4 (induced by source paper "learning to acquire information"):
  with members:
    - "preference elicitation for general random utility models"
    - "building high level features using large scale unsupervised learning"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-5 (induced by source paper "efficient touch based localization through submodularity"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "near optimal nonmyopic value of information in graphical models"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "online planning algorithms for pomdps"
    - "finding approximate pomdp solutions through belief compression"

Connected through hyperedge HE-6 (induced by source paper "active diagnosis via auc maximization an efficient approach for multiple fault identification in large scale noisy networks"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "loopy belief propagation for approximate inference an empirical study"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-7 (induced by source paper "near optimal bayesian active learning with noisy observations"):
  with members:
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"
    - "optimal value of information in graphical models"

Connected through hyperedge HE-8 (induced by source paper "large scale probabilistic available bandwidth estimation"):
  with members:
    - "a system theoretic approach to bandwidth estimation"
    - "sufficient conditions for convergence of the sum product 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)