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: "online decision making in crowdsourcing markets theoretical challenges position paper"
  Abstract: Over the past decade, crowdsourcing has emerged as a cheap and efficient method of obtaining solutions to simple tasks that are difficult for computers to solve but possible for humans. The popularity and promise of crowdsourcing markets has led to both empirical and theoretical research on the design of algorithms to optimize various aspects of these markets, such as the pricing and assignment of tasks. Much of the existing theoretical work on crowdsourcing markets has focused on problems that fall into the broad category of online decision making; task requesters or the crowdsourcing platform itself make repeated decisions about prices to set, workers to filter out, problems to assign to specific workers, or other things. Often these decisions are complex, requiring algorithms that learn about the distribution of available tasks or workers over time and take into account the strategic (or sometimes irrational) behavior of workers. #R##N#As human computation grows into its own field, the time is ripe to address these challenges in a principled way. However, it appears very difficult to capture all pertinent aspects of crowdsourcing markets in a single coherent model. In this paper, we reflect on the modeling issues that inhibit theoretical research on online decision making for crowdsourcing, and identify some steps forward. This paper grew out of the authors' own frustration with these issues, and we hope it will encourage the community to attempt to understand, debate, and ultimately address them. #R##N#The authors welcome feedback for future revisions of this paper.

Connected through hyperedge HE-1 (induced by source paper "linear contextual bandits with global constraints and objective"):
  with members:
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "bandits with concave rewards and convex knapsacks"
    - "fast algorithms for online stochastic convex programming"
    - "resourceful contextual bandits"
    - "how the experts algorithm can help solve lps online"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a near optimal dynamic learning algorithm for online matching problems with concave returns"
    - "bandits with knapsacks"

Connected through hyperedge HE-2 (induced by source paper "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "bandits with knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "dynamic pricing with limited supply"

Connected through hyperedge HE-3 (induced by source paper "contextual bandits with global constraints and objective"):
  with members:
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "bandits with concave rewards and convex knapsacks"
    - "fast algorithms for online stochastic convex programming"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "bandits with knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"

Connected through hyperedge HE-4 (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"
    - "the ratio index for budgeted learning with applications"

Connected through hyperedge HE-5 (induced by source paper "bandits with concave rewards and convex knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "bandits with knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "multi armed bandits in metric spaces"

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.SI(Social and Information Networks)