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: "data driven rank breaking for efficient rank aggregation"
  Abstract: Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied to efficient algorithms tailored for independent paired comparisons. However, due to the ignored dependencies in the data, naive rank-breaking approaches can result in inconsistent estimates. The key idea to produce accurate and consistent estimates is to treat the pairwise comparisons unequally, depending on the topology of the collected data. In this paper, we provide the optimal rank-breaking estimator, which not only achieves consistency but also achieves the best error bound. This allows us to characterize the fundamental tradeoff between accuracy and complexity. Further, the analysis identifies how the accuracy depends on the spectral gap of a corresponding comparison graph.

Connected through hyperedge HE-1 (induced by source paper "regret minimization in stochastic contextual dueling bandits"):
  with members:
    - "combinatorial bandits with relative feedback"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "multi dueling bandits with dependent arms"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "double thompson sampling for dueling bandits"
    - "copeland dueling bandits"

Connected through hyperedge HE-2 (induced by source paper "best item learning in random utility models with subset choices"):
  with members:
    - "active ranking with subset wise preferences"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "multi dueling bandits with dependent arms"
    - "competitive analysis of the top k ranking problem"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-3 (induced by source paper "combinatorial bandits with relative feedback"):
  with members:
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "multi dueling bandits with dependent arms"
    - "thompson sampling for the mnl bandit"
    - "dcm bandits learning to rank with multiple clicks"
    - "multi dueling bandits and their application to online ranker evaluation"

Connected through hyperedge HE-4 (induced by source paper "learning mixtures of plackett luce models from structured partial orders"):
  with members:
    - "a cost effective framework for preference elicitation and aggregation"
    - "learning mixtures of plackett luce models"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "efficient probabilistic inference with partial ranking queries"
    - "active ranking using pairwise comparisons"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "from pac to instance optimal sample complexity in the plackett luce model"):
  with members:
    - "active ranking with subset wise preferences"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "multi dueling bandits with dependent arms"
    - "competitive analysis of the top k ranking problem"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "noisy sorting without resampling"

Connected through hyperedge HE-6 (induced by source paper "fast and robust rank aggregation against model misspecification"):
  with members:
    - "generalizing to unseen domains via adversarial data augmentation"
    - "wasserstein distributional robustness and regularization in statistical learning"
    - "peer grading in a course on algorithms and data structures machine learning algorithms do not improve over simple baselines"
    - "methods for ordinal peer grading"

Connected through hyperedge HE-7 (induced by source paper "active ranking with subset wise preferences"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "contextual dueling bandits"
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"
    - "rank aggregation via nuclear norm minimization"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-8 (induced by source paper "pac battling bandits in the plackett luce model"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "budget constrained multi armed bandits with multiple plays"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "competitive analysis of the top k ranking problem"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "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.LG(Machine Learning)