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: "minimally invasive randomization for collecting unbiased preferences from clickthrough logs"
  Abstract: Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well known that the probability of a user clicking on a result is strongly biased toward documents presented higher in the result set irrespective of relevance. We introduce a simple method to modify the presentation of search results that provably gives relevance judgments that are unaffected by presentation bias under reasonable assumptions. We validate this property of the training data in interactive real world experiments. Finally, we show that using these unbiased relevance judgments learning methods can be guaranteed to converge to an ideal ranking given sufficient data.

Connected through hyperedge HE-1 (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-2 (induced by source paper "production ranking systems a review"):
  with members:
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "practical constrained optimization of auction mechanisms in e commerce sponsored search advertising"
    - "eenmf an end to end neural matching framework for e commerce sponsored search"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "visually aware fashion recommendation and design with generative image models"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"

Connected through hyperedge HE-3 (induced by source paper "quantifying voter biases in online platforms an instrumental variable approach"):
  with members:
    - "quantifying the impact of cognitive biases in question answering systems"
    - "the size conundrum why online knowledge markets can fail at scale"
    - "guessthekarma a game to assess social rating systems"
    - "when sheep shop measuring herding effects in product ratings with natural experiments"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "consumers and curators browsing and voting patterns on reddit"
    - "rating effects on social news posts and comments"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-4 (induced by source paper "position bias estimation for unbiased learning to rank in ecommerce search"):
  with members:
    - "estimating position bias without intrusive interventions"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-5 (induced by source paper "recommending dream jobs in a biased real world"):
  with members:
    - "equality of opportunity in supervised learning"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "images don t lie transferring deep visual semantic features to large scale multimodal learning to rank"):
  with members:
    - "texture synthesis using convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-7 (induced by source paper "on search engine evaluation metrics"):
  with members:
    - "the retrieval effectiveness of web search engines considering results descriptions"
    - "predicting user preferences"
    - "model adaptation via model interpolation and boosting for web search ranking"

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)