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: "an improved multileaving algorithm for online ranker evaluation"
  Abstract: Online ranker evaluation is a key challenge in information retrieval. An important task in the online evaluation of rankers is using implicit user feedback for inferring preferences between rankers. Interleaving methods have been found to be efficient and sensitive, i.e. they can quickly detect even small differences in quality. It has recently been shown that multileaving methods exhibit similar sensitivity but can be more efficient than interleaving methods. This paper presents empirical results demonstrating that existing multileaving methods either do not scale well with the number of rankers, or, more problematically, can produce results which substantially differ from evaluation measures like NDCG. The latter problem is caused by the fact that they do not correctly account for the similarities that can occur between rankers being multileaved. We propose a new multileaving method for handling this problem and demonstrate that it substantially outperforms existing methods, in some cases reducing errors by as much as 50%.

Connected through hyperedge HE-1 (induced by source paper "on the value of bandit feedback for offline recommender system evaluation"):
  with members:
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"
    - "effective evaluation using logged bandit feedback from multiple loggers"
    - "translation based recommendation"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-2 (induced by source paper "greedy optimized multileaving for personalization"):
  with members:
    - "algorithms and system architecture for immediate personalized news recommendations"

Connected through hyperedge HE-3 (induced by source paper "dependencies formalising semantic catenae for information retrieval"):
  with members:
    - "fixed versus dynamic co occurrence windows in textrank term weights for information retrieval"
    - "preliminary experiments using subjective logic for the polyrepresentation of information needs"
    - "rhetorical relations for information retrieval"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "part of speech based term weighting for information retrieval"
    - "learning to generate compositional color descriptions"
    - "adaptive distributional extensions to dfr ranking"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"

Connected through hyperedge HE-4 (induced by source paper "evaluation measures for relevance and credibility in ranked lists"):
  with members:
    - "multi dueling bandits and their application to online ranker evaluation"
    - "a study of factuality objectivity and relevance three desiderata in large scale information retrieval"
    - "assessing the quality of web content"

Connected through hyperedge HE-5 (induced by source paper "sensitive and scalable online evaluation with theoretical guarantees"):
  with members:
    - "introducing letor 4 0 datasets"

Connected through hyperedge HE-6 (induced by source paper "a study of factuality objectivity and relevance three desiderata in large scale information retrieval"):
  with members:
    - "rhetorical relations for information retrieval"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "entropy and graph based modelling of document coherence using discourse entities an application"
    - "non compositional term dependence for information retrieval"
    - "efficient and effective spam filtering and re ranking for large web datasets"

Connected through hyperedge HE-7 (induced by source paper "multi dueling bandits and their application to online ranker evaluation"):
  with members:
    - "copeland dueling bandits"
    - "contextual dueling bandits"
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"

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)