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: "balancing speed and quality in online learning to rank for information retrieval"
  Abstract: In Online Learning to Rank (OLTR) the aim is to find an optimal ranking model by interacting with users. When learning from user behavior, systems must interact with users while simultaneously learning from those interactions. Unlike other Learning to Rank (LTR) settings, existing research in this field has been limited to linear models. This is due to the speed-quality tradeoff that arises when selecting models: complex models are more expressive and can find the best rankings but need more user interactions to do so, a requirement that risks frustrating users during training. Conversely, simpler models can be optimized on fewer interactions and thus provide a better user experience, but they will converge towards suboptimal rankings. This tradeoff creates a deadlock, since novel models will not be able to improve either the user experience or the final convergence point, without sacrificing the other.    Our contribution is twofold. First, we introduce a fast OLTR model called Sim-MGD that addresses the speed aspect of the speed-quality tradeoff. Sim-MGD ranks documents based on similarities with reference documents. It converges rapidly and, hence, gives a better user experience but it does not converge towards the optimal rankings. Second, we contribute Cascading Multileave Gradient De- scent (C-MGD) for OLTR that directly addresses the speed-quality tradeoff by using a cascade that enables combinations of the best of two worlds: fast learning and high quality final convergence. C-MGD can provide the better user experience of Sim-MGD while maintaining the same convergence as the state-of-the-art MGD model. This opens the door for future work to design new models for OLTR without having to deal with the speed-quality tradeoff.

Connected through hyperedge HE-1 (induced by source paper "safe exploration for optimizing contextual bandits"):
  with members:
    - "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"
    - "boltzmann exploration done right"
    - "unbiased learning to rank with biased feedback"
    - "risk aware algorithms for adversarial contextual bandits"
    - "off policy evaluation for slate recommendation"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "introducing letor 4 0 datasets"

Connected through hyperedge HE-2 (induced by source paper "unbiased learning to rank counterfactual and online approaches"):
  with members:
    - "optimizing ranking models in an online setting"
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "differentiable unbiased online learning to rank"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "consistent position bias estimation without online interventions for learning to rank"
    - "unbiased learning to rank with biased feedback"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"

Connected through hyperedge HE-3 (induced by source paper "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"):
  with members:
    - "optimizing ranking models in an online setting"
    - "differentiable unbiased online learning to rank"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "counterfactual learning to rank for additive metrics and deep models"
    - "sensitive and scalable online evaluation with theoretical guarantees"
    - "unbiased learning to rank with biased feedback"
    - "off policy evaluation for slate recommendation"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-4 (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"
    - "online learning to rank in stochastic click models"
    - "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-5 (induced by source paper "optimizing ranking models in an online setting"):
  with members:
    - "efficient exploration of gradient space for online learning to rank"
    - "differentiable unbiased online learning to rank"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "sensitive and scalable online evaluation with theoretical guarantees"
    - "unbiased learning to rank with biased feedback"
    - "introducing letor 4 0 datasets"

Connected through hyperedge HE-6 (induced by source paper "differentiable unbiased online learning to rank"):
  with members:
    - "sensitive and scalable online evaluation with theoretical guarantees"
    - "online learning to rank in stochastic click models"
    - "on application of learning to rank for e commerce search"
    - "introducing letor 4 0 datasets"

Connected through hyperedge HE-7 (induced by source paper "efficient exploration of gradient space for online learning to rank"):
  with members:
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits learning to rank in the cascade model"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)