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: "on application of learning to rank for e commerce search"
  Abstract: E-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines, and is thus also a key technology for E-Com search. While the use of LETOR for web search has been well studied, its use for E-Com search has not yet been well explored. In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-to-cart ratios, order rates, and revenue. We study these new challenges using experiments on industry data sets and report several interesting findings that can provide guidance on how to optimally apply LETOR to E-Com search: First, popularity-based features defined solely on product items are very useful and LETOR methods were able to effectively optimize their combination with relevance-based features. Second, query attribute sparsity raises challenges for LETOR, and selecting features to reduce/avoid sparsity is beneficial. Third, while crowdsourcing is often useful for obtaining relevance judgments for Web search, it does not work as well for E-Com search due to difficulty in eliciting sufficiently fine grained relevance judgments. Finally, among the multiple feedback signals, the order rate is found to be the most robust training objective, followed by click rate, while add-to-cart ratio seems least robust, suggesting that an effective practical strategy may be to initially use click rates for training and gradually shift to using order rates as they become available.

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 "explainable product search with a dynamic relation embedding model"):
  with members:
    - "conversational product search based on negative feedback"
    - "leverage implicit feedback for context aware product search"
    - "a zero attention model for personalized product search"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning a deep listwise context model for ranking refinement"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"

Connected through hyperedge HE-3 (induced by source paper "neural ir meets graph embedding a ranking model for product search"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "entity duet neural ranking understanding the role of knowledge graph semantics in neural information retrieval"
    - "graph embedding techniques applications and performance a survey"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "semantic entity retrieval toolkit"
    - "ensemble methods for personalized e commerce search challenge at cikm cup 2016"
    - "mimicking word embeddings using subword rnns"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "a zero attention model for personalized product search"):
  with members:
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "attentive long short term preference modeling for personalized product search"
    - "ensemble methods for personalized e commerce search challenge at cikm cup 2016"
    - "attention is all you need"
    - "an ensemble based approach to click through rate prediction for promoted listings at etsy"
    - "neural collaborative filtering"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-5 (induced by source paper "a domain generalization perspective on listwise context modeling"):
  with members:
    - "globally optimized mutual influence aware ranking in e commerce search"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "generalizing across domains via cross gradient training"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "learning to generalize meta learning for domain generalization"
    - "unified deep supervised domain adaptation and generalization"

Connected through hyperedge HE-6 (induced by source paper "leverage implicit feedback for context aware product search"):
  with members:
    - "sequence aware recommender systems"
    - "learning a deep listwise context model for ranking refinement"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "session aware information embedding for e commerce product recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "unbiased learning to rank with biased feedback"
    - "general factorization framework for context aware recommendations"

Connected through hyperedge HE-7 (induced by source paper "conversational product search based on negative feedback"):
  with members:
    - "a study of context dependencies in multi page product search"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "conversational recommender system"
    - "iterative relevance feedback for answer passage retrieval with passage level semantic match"
    - "attentive memory networks efficient machine reading for conversational search"
    - "learning latent vector spaces for product search"
    - "inferring networks of substitutable and complementary products"
    - "when relevance is not enough promoting visual attractiveness for fashion e commerce"

Connected through hyperedge HE-8 (induced by source paper "counterfactual learning from logs for improved ranking of e commerce products"):
  with members:
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "adam a method for stochastic optimization"
    - "online ranking with top 1 feedback"

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)