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 "explainable fashion recommendation with joint outfit matching and comment generation"):
  Abstract: Most previous work on fashion recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proved effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural fashion recommendation (NFR), that simultaneously provides fashion recommendations and generates abstractive comments. NFR consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features of outfits. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NFR achieves significant improvements over state-of-the-art baselines for fashion recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.

Includes members:
  Member "explainable recommendation via multi task learning in opinionated text data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "measuring semantic coherence of a conversation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the technological gap between virtual assistants and recommendation systems")
  Member "learning to generate reviews and discovering sentiment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "neural compatibility modeling with attentive knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep look into neural ranking models for information retrieval")
  Member "selective encoding for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization")
  Member "deep recurrent generative decoder for abstractive text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "advances in collaborative filtering and ranking")

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)