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 "attentive long short term preference modeling for personalized product search"):
  Abstract: E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-term ones and short-term ones. The former refers to user' inherent purchasing bias and evolves slowly. By contrast, the latter reflects users' purchasing inclination in a relatively short period. They both affect users' current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long- and short-term user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users' current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.

Includes members:
  Member "review driven answer generation for product related questions in e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantifying and alleviating the language prior problem in visual question answering")
  Member "mmalfm explainable recommendation by leveraging reviews and images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "aspect aware latent factor model rating prediction with ratings and reviews":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective")
  Member "deeprank a new deep architecture for relevance ranking in information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "ensemble methods for personalized e commerce search challenge at cikm cup 2016":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainable product search with a dynamic relation embedding model")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in collaborative filtering and ranking")
  Member "neural attentive session based recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attentive item2vec neural attentive user representations")
  Member "attentional factorization machines learning the weight of feature interactions via attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)