human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "estimating position bias without intrusive interventions"):
  Abstract: Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counterfactual learning-to-rank (LTR) approaches \citeJoachims/etal/17a can provably overcome presentation bias when observation propensities are known, it remains to show how to effectively estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. First, we show how to harvest a specific type of intervention data from historic feedback logs of multiple different ranking functions, and show that this data is sufficient for consistent propensity estimation in the position-based model. Second, we propose a new extremum estimator that makes effective use of this data. In an empirical evaluation, we find that the new estimator provides superior propensity estimates in two real-world systems -- Arxiv Full-text Search and Google Drive Search. Beyond these two points, we find that the method is robust to a wide range of settings in simulation studies.

Includes members:
  Member "unbiased learning to rank with unbiased propensity estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "handling position bias for unbiased learning to rank in hotels search")
  Member "counterfactual learning to rank for additive metrics and deep models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions")
  Member "effective evaluation using logged bandit feedback from multiple loggers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "task selection policies for multitask learning")
  Member "unbiased learning to rank with biased feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe exploration for optimizing contextual bandits")
  Member "recommendations as treatments debiasing learning and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond")
  Member "counterfactual estimation and optimization of click metrics for search engines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "balanced linear contextual bandits")
  Member "unbiased offline evaluation of contextual bandit based news article recommendation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised contextual bandits in computer vision")

Question: Please tell me which class the source paper 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)