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: "the ratio index for budgeted learning with applications"
  Abstract: In the budgeted learning problem, we are allowed to experiment on a set of alternatives (given a fixed experimentation budget) with the goal of picking a single alternative with the largest possible expected payoff. Approximation algorithms for this problem were developed by Guha and Munagala by rounding a linear program that couples the various alternatives together. In this paper we present an index for this problem, which we call the ratio index, which also guarantees a constant factor approximation. Index-based policies have the advantage that a single number (i.e. the index) can be computed for each alternative irrespective of all other alternatives, and the alternative with the highest index is experimented upon. This is analogous to the famous Gittins index for the discounted multi-armed bandit problem. #R##N#The ratio index has several interesting structural properties. First, we show that it can be computed in strongly polynomial time. Second, we show that with the appropriate discount factor, the Gittins index and our ratio index are constant factor approximations of each other, and hence the Gittins index also gives a constant factor approximation to the budgeted learning problem. Finally, we show that the ratio index can be used to create an index-based policy that achieves an O(1)-approximation for the finite horizon version of the multi-armed bandit problem. Moreover, the policy does not require any knowledge of the horizon (whereas we compare its performance against an optimal strategy that is aware of the horizon). This yields the following surprising result: there is an index-based policy that achieves an O(1)-approximation for the multi-armed bandit problem, oblivious to the underlying discount factor.

Connected through hyperedge HE-1 (induced by source paper "bayesian incentive compatible bandit exploration"):
  with members:
    - "fairness in learning classic and contextual bandits"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "economic recommendation systems"
    - "online learning with feedback graphs beyond bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandits with knapsacks"
    - "from bandits to experts a tale of domination and independence"

Connected through hyperedge HE-2 (induced by source paper "incentivizing exploration with heterogeneous value of money"):
  with members:
    - "bayesian incentive compatible bandit exploration"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "online decision making in crowdsourcing markets theoretical challenges position paper"

Connected through hyperedge HE-3 (induced by source paper "approximation algorithms for bayesian multi armed bandit problems"):
  with members:
    - "active model selection"
    - "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"
    - "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 "dynamic pricing with limited supply"):
  with members:
    - "bandits with knapsacks"
    - "adaptive crowdsourcing algorithms for the bandit survey problem"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "mechanism design via correlation gap"
    - "truthful mechanisms with implicit payment computation"
    - "x armed bandits"
    - "approximation schemes for sequential posted pricing in multi unit auctions"
    - "contextual bandits with similarity information"

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"
    - "active model selection"
    - "extensive games with possibly unaware players"

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.DS(Data Structures and Algorithms)