human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning to ask question based sequential bayesian product search"):
  Abstract: Product search is generally recognized as the first and foremost stage of online shopping and thus significant for users and retailers of e-commerce. Most of the traditional retrieval methods use some similarity functions to match the user's query and the document that describes a product, either directly or in a latent vector space. However, user queries are often too general to capture the minute details of the specific product that a user is looking for. In this paper, we propose a novel interactive method to effectively locate the best matching product. The method is based on the assumption that there is a set of candidate questions for each product to be asked. In this work, we instantiate this candidate set by making the hypothesis that products can be discriminated by the entities that appear in the documents associated with them. We propose a Question-based Sequential Bayesian Product Search method, QSBPS, which directly queries users on the expected presence of entities in the relevant product documents. The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users. The experimental results show that the proposed method can greatly improve the performance of product search compared to the state-of-the-art baselines.

Includes members:
  Member "technology assisted reviews finding the last few relevant documents by asking yes no questions to reviewers":
    also connected through other hyperedges:
      (none)
  Member "playing 20 question game with policy based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interactive classification by asking informative questions")
  Member "attentive long short term preference modeling for personalized product search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering")
  Member "learning to ask knowledge acquisition via 20 questions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interactive classification by asking informative questions")
  Member "joint modeling and optimization of search and recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "macaw an extensible conversational information seeking platform")
  Member "conversational recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "word entity duet representations for document ranking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "learning latent vector spaces for product search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quality aware skill translation models for expert finding on stackoverflow")

Question: Please tell me which class the source paper 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)