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: "effective evaluation using logged bandit feedback from multiple loggers"
  Abstract: Accurately evaluating new policies (e.g. ad-placement models, ranking functions, recommendation functions) is one of the key prerequisites for improving interactive systems. While the conventional approach to evaluation relies on online A/B tests, recent work has shown that counterfactual estimators can provide an inexpensive and fast alternative, since they can be applied offline using log data that was collected from a different policy fielded in the past. In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies. This question is of great relevance in practice, since policies get updated frequently in most online systems. We show that naively combining data from multiple logging policies can be highly suboptimal. In particular, we find that the standard Inverse Propensity Score (IPS) estimator suffers especially when logging and target policies diverge -- to a point where throwing away data improves the variance of the estimator. We therefore propose two alternative estimators which we characterize theoretically and compare experimentally. We find that the new estimators can provide substantially improved estimation accuracy.

Connected through hyperedge HE-1 (induced by source paper "task selection policies for multitask learning"):
  with members:
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "evaluating the supervised and zero shot performance of multi lingual translation models"
    - "autosem automatic task selection and mixing in multi task learning"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "off the shelf unsupervised nmt"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "improving evolutionary strategies with generative neural networks"):
  with members:
    - "neural generative models for global optimization with gradients"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "proximal policy optimization algorithms"
    - "density estimation using real nvp"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "benchmarking deep reinforcement learning for continuous control"
    - "the cma evolution strategy a tutorial"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-3 (induced by source paper "estimating position bias without intrusive interventions"):
  with members:
    - "unbiased learning to rank with unbiased propensity estimation"
    - "counterfactual learning to rank for additive metrics and deep models"
    - "unbiased learning to rank with biased feedback"
    - "recommendations as treatments debiasing learning and evaluation"
    - "counterfactual estimation and optimization of click metrics for search engines"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"

Connected through hyperedge HE-4 (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"
    - "translation based recommendation"
    - "an improved multileaving algorithm for online ranker evaluation"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-5 (induced by source paper "active learning with logged data"):
  with members:
    - "recommendations as treatments debiasing learning and evaluation"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "efficient and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "minimax analysis of active learning"
    - "online importance weight aware updates"

Connected through hyperedge HE-6 (induced by source paper "efficient counterfactual learning from bandit feedback"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "learning from logged implicit exploration data"
    - "a contextual bandit approach to personalized news article recommendation"

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)