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: "merge double thompson sampling for large scale online ranker evaluation"
  Abstract: Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaved comparison methods, how to effectively choose the pair of rankers to generate the result list without degrading the user experience too much can be formalized as a K-armed dueling bandit problem, which is an online partial-information learning framework, where feedback comes in the form of pair-wise preferences. A commercial search system may evaluate a large number of rankers concurrently, and scaling effectively in the presence of numerous rankers has not been fully studied. #R##N#In this paper, we focus on solving the large-scale online ranker evaluation problem under the so-called Condorcet assumption, where there exists an optimal ranker that is preferred to all other rankers. We propose Merge Double Thompson Sampling (MergeDTS), which first utilizes a divide-and-conquer strategy that localizes the comparisons carried out by the algorithm to small batches of rankers, and then employs the Thompson Sampling (TS) to reduce the comparisons between suboptimal rankers inside these small batches. The effectiveness (regret) and efficiency (time complexity) of MergeDTS are extensively evaluated using examples from the domain of online evaluation for web search. Our main finding is that for large-scale Condorcet ranker evaluation problems MergeDTS outperforms the state-of-the-art dueling bandit algorithms.

Connected through hyperedge HE-1 (induced by source paper "a contextual bandit approach to online learning to rank for relevance and diversity"):
  with members:
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "bubblerank safe online learning to re rank via implicit click feedback"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "differentiable unbiased online learning to rank"
    - "online learning to rank in stochastic click models"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"

Connected through hyperedge HE-2 (induced by source paper "cascading non stationary bandits online learning to rank in the non stationary cascade model"):
  with members:
    - "online learning to rank in stochastic click models"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits learning to rank in the cascade model"
    - "matroid bandits fast combinatorial optimization with learning"

Connected through hyperedge HE-3 (induced by source paper "online learning to rank with list level feedback for image filtering"):
  with members:
    - "efficient exploration of gradient space for online learning to rank"
    - "constructing an interaction behavior model for web image search"
    - "incremental sparse bayesian ordinal regression"
    - "why people search for images using web search engines"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "cascading bandits learning to rank in the cascade model"
    - "adam a method for stochastic optimization"

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.IR(Information Retrieval)