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: "active model selection"
  Abstract: Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins not with a training sample, but instead with resources that it can use to obtain information to help identify the optimal model. To better understand this task, this paper presents and analyses the simplified "(budgeted) active model selection" version, which captures the pure exploration aspect of many active learning problems in a clean and simple problem formulation. Here the learner can use a fixed budget of "model probes" (where each probe evaluates the specified model on a random indistinguishable instance) to identify which of a given set of possible models has the highest expected accuracy. Our goal is a policy that sequentially determines which model to probe next, based on the information observed so far. We present a formal description of this task, and show that it is NPhard in general. We then investigate a number of algorithms for this task, including several existing ones (eg, "Round-Robin", "Interval Estimation", "Gittins") as well as some novel ones (e.g., "Biased-Robin"), describing first their approximation properties and then their empirical performance on various problem instances. We observe empirically that the simple biased-robin algorithm significantly outperforms the other algorithms in the case of identical costs and priors.

Connected through hyperedge HE-1 (induced by source paper "selecting computations theory and applications"):
  with members:
    - "reasoning about beliefs and actions under computational resource constraints"
    - "mcts based on simple regret"

Connected through hyperedge HE-2 (induced by source paper "attribute efficient linear regression with data dependent sampling"):
  with members:
    - "efficient learning with partially observed attributes"

Connected through hyperedge HE-3 (induced by source paper "approximation algorithms for bayesian multi armed bandit problems"):
  with members:
    - "adaptive submodular optimization under matroid constraints"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "the ratio index for budgeted learning with applications"
    - "sequential design of experiments via linear programming"
    - "sponsored search auctions with markovian users"
    - "approximation algorithms for restless bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "adaptive crowdsourcing algorithms for the bandit survey problem"):
  with members:
    - "dynamic pricing with limited supply"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-5 (induced by source paper "mdps with unawareness"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "hierarchical solution of markov decision processes using macro actions"
    - "the ratio index for budgeted learning with applications"
    - "extensive games with possibly unaware players"

Connected through hyperedge HE-6 (induced by source paper "efficient learning with partially observed attributes"):
  with members:
    - "optimal algorithms for ridge and lasso regression with partially observed attributes"
    - "online learning of noisy data with kernels"
    - "importance weighted active learning"

Connected through hyperedge HE-7 (induced by source paper "sequential design of experiments via linear programming"):
  with members:
    - "near optimal nonmyopic value of information in graphical models"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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.LG(Machine Learning)