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: "unbiased learning to rank with biased feedback"
  Abstract: Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a Propensity-Weighted Ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. In contrast to most conventional approaches to de-bias the data using click models, this allows training of ranking functions even in settings where queries do not repeat. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model misspecification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.

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"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "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 "handling position bias for unbiased learning to rank in hotels search"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "estimating position bias without intrusive interventions"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "correcting for selection bias in learning to rank systems"):
  with members:
    - "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"
    - "unbiased lambdamart an unbiased pairwise learning to rank algorithm"
    - "the deconfounded recommender a causal inference approach to recommendation"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "a general framework for counterfactual learning to rank"
    - "recommendations as treatments debiasing learning and evaluation"

Connected through hyperedge HE-4 (induced by source paper "improving deep learning for airbnb search"):
  with members:
    - "estimating position bias without intrusive interventions"
    - "applying deep learning to airbnb search"
    - "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-5 (induced by source paper "eliminating search intent bias in learning to rank"):
  with members:
    - "context aware image annotation in active learning"
    - "conversational structure aware and context sensitive topic model for online discussions"
    - "knowledge guided text structuring in clinical trials"
    - "wikipedia based semantic interpretation for natural language processing"

Connected through hyperedge HE-6 (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"
    - "estimating position bias without intrusive interventions"
    - "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"

Connected through hyperedge HE-7 (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"
    - "estimating position bias without intrusive interventions"
    - "policy learning for fairness in ranking"

Connected through hyperedge HE-8 (induced by source paper "selection bias explorations and debias methods for natural language sentence matching datasets"):
  with members:
    - "sentence modeling via multiple word embeddings and multi level comparison for semantic textual similarity"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "natural language inference over interaction space"

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)