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 learning to rank in stochastic click models"
  Abstract: Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts with a list of documents. Though these results are significant, their impact on practice is limited, because all proposed algorithms are designed for specific click models and lack convergence guarantees in other models. In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models. The class encompasses two most fundamental click models, the cascade and position-based models. We derive a gap-dependent upper bound on the $T$-step regret of BatchRank and evaluate it on a range of web search queries. We observe that BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an existing algorithm for the cascade model.

Connected through hyperedge HE-1 (induced by source paper "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"):
  with members:
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "bandit online learning with unknown delays"
    - "thompson sampling algorithms for cascading bandits"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "combinatorial bandits revisited"
    - "cascading bandits learning to rank in the cascade model"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

Connected through hyperedge HE-2 (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"
    - "merge double thompson sampling for large scale online ranker evaluation"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "differentiable unbiased online learning to rank"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"

Connected through hyperedge HE-3 (induced by source paper "variance reduction in gradient exploration for online learning to rank"):
  with members:
    - "efficient exploration of gradient space for online learning to rank"
    - "differentiable unbiased online learning to rank"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits learning to rank in the cascade model"
    - "introducing letor 4 0 datasets"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "cascading non stationary bandits online learning to rank in the non stationary cascade model"):
  with members:
    - "merge double thompson sampling for large scale online ranker evaluation"
    - "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-5 (induced by source paper "thompson sampling for cascading bandits"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a tutorial on thompson sampling"
    - "thompson sampling for the mnl bandit"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"

Connected through hyperedge HE-6 (induced by source paper "online newton step algorithm with estimated gradient"):
  with members:
    - "online learning a comprehensive survey"
    - "stochastic optimization with bandit sampling"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "on graduated optimization for stochastic non convex problems"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning to rank in e commerce search engine formalization analysis and application"):
  with members:
    - "stochastic rank 1 bandits"
    - "multiple play bandits in the position based model"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-8 (induced by source paper "differentiable unbiased online learning to rank"):
  with members:
    - "sensitive and scalable online evaluation with theoretical guarantees"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "on application of learning to rank for e commerce search"
    - "introducing letor 4 0 datasets"

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)