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 "visually explainable recommendation"):
  Abstract: Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual patterns of image (e.g., hue, texture, color, etc) can be highly personalized, and this provides us with highly discriminative features to make personalized recommendations. #R##N#Previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors, which are adopted by a recommendation component to assist personalized user/item profiling and recommendation. However, such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended, and thus weakens the explainability of recommendation systems. #R##N#As a step towards explainable recommendation models, we propose visually explainable recommendation based on attentive neural networks to model the user attention on images, under the supervision of both implicit feedback and textual reviews. By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner. Experimental results show that our models are not only able to improve the recommendation performance, but also can provide persuasive visual explanations for the users to take the recommendations.

Includes members:
  Member "deep learning based large scale visual recommendation and search for e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "featurenms non maximum suppression by learning feature embeddings")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in collaborative filtering and ranking")
  Member "neural rating regression with abstractive tips generation for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning")
  Member "explainable recommendation theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable product search with a dynamic relation embedding model")
  Member "a visual and textual recurrent neural network for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interest related item similarity model based on multimodal data for top n recommendation")
  Member "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "interpretable recommendations via overlapping co clusters":
    also connected through other hyperedges:
      (none)
  Member "explaining reviews and ratings with paco poisson additive co clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "heterogeneous deep graph infomax")

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)