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: "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.

Connected through hyperedge HE-1 (induced by source paper "fairrec two sided fairness for personalized recommendations in two sided platforms"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "incremental fairness in two sided market platforms on smoothly updating recommendations"
    - "multi stakeholder recommendation and its connection to multi sided fairness"
    - "comparing approximate relaxations of envy freeness"
    - "equity of attention amortizing individual fairness in rankings"
    - "almost envy free allocations with connected bundles"
    - "fair division under cardinality constraints"
    - "groupwise maximin fair allocation of indivisible goods"

Connected through hyperedge HE-2 (induced by source paper "handling position bias for unbiased learning to rank in hotels search"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "position bias estimation for unbiased learning to rank in ecommerce search"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "applying deep learning to airbnb search"
    - "direct estimation of position bias for unbiased learning to rank without intervention"
    - "consistent position bias estimation without online interventions for learning to rank"
    - "visual discovery at pinterest"
    - "related pins at pinterest the evolution of a real world recommender system"

Connected through hyperedge HE-3 (induced by source paper "improving deep learning for airbnb search"):
  with members:
    - "applying deep learning to airbnb search"
    - "unbiased learning to rank with biased feedback"
    - "wide deep learning for recommender systems"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "fairness in recommendation ranking through pairwise comparisons"):
  with members:
    - "putting fairness principles into practice challenges metrics and improvements"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "policy learning for fairness in ranking"
    - "equity of attention amortizing individual fairness in rankings"
    - "selection problems in the presence of implicit bias"
    - "mitigating unwanted biases with adversarial learning"
    - "top k off policy correction for a reinforce recommender system"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-5 (induced by source paper "fair learning to rank from implicit feedback"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair logistic regression an adversarial perspective"
    - "the few get richer a surprising consequence of popularity based rankings"
    - "intervention harvesting for context dependent examination bias estimation"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "policy learning for fairness in ranking"
    - "pairwise fairness for ranking and regression"

Connected through hyperedge HE-6 (induced by source paper "policy learning for fairness in ranking"):
  with members:
    - "intervention harvesting for context dependent examination bias estimation"
    - "equity of attention amortizing individual fairness in rankings"
    - "reducing disparate exposure in ranking a learning to rank approach"
    - "fairness of exposure in rankings"
    - "ranking with fairness constraints"
    - "learning non discriminatory predictors"
    - "fa ir a fair top k ranking algorithm"
    - "designing fair ranking schemes"

Connected through hyperedge HE-7 (induced by source paper "intervention harvesting for context dependent examination bias estimation"):
  with members:
    - "unbiased learning to rank with unbiased propensity estimation"
    - "a click sequence model for web search"
    - "counterfactual learning to rank for additive metrics and deep models"
    - "a general framework for counterfactual learning to rank"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-8 (induced by source paper "position bias estimation for unbiased learning to rank in ecommerce search"):
  with members:
    - "unbiased learning to rank with unbiased propensity estimation"
    - "unbiased learning to rank with biased feedback"
    - "minimally invasive randomization for collecting unbiased preferences from clickthrough logs"

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)