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: "learning latent vector spaces for product search"
  Abstract: We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.

Connected through hyperedge HE-1 (induced by source paper "quality aware skill translation models for expert finding on stackoverflow"):
  with members:
    - "unsupervised efficient and semantic expertise retrieval"
    - "adadelta an adaptive learning rate method"

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"
    - "on application of learning to rank for e commerce search"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-5 (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"
    - "on application of learning to rank for e commerce search"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-6 (induced by source paper "learning to ask question based sequential bayesian product search"):
  with members:
    - "technology assisted reviews finding the last few relevant documents by asking yes no questions to reviewers"
    - "playing 20 question game with policy based reinforcement learning"
    - "attentive long short term preference modeling for personalized product search"
    - "learning to ask knowledge acquisition via 20 questions"
    - "joint modeling and optimization of search and recommendation"
    - "conversational recommender system"
    - "word entity duet representations for document ranking"
    - "autonomy and reliability of continuous active learning for technology assisted review"

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"
    - "on application of learning to rank for e commerce 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 "wiser a semantic approach for expert finding in academia based on entity linking"):
  with members:
    - "semantic entity retrieval toolkit"
    - "unsupervised efficient and semantic expertise retrieval"
    - "learning to rank academic experts in the dblp dataset"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "fast and accurate annotation of short texts with wikipedia pages"

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)