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 "graphsw a training protocol based on stage wise training for gnn based recommender model"):
  Abstract: Recently, researchers utilize Knowledge Graph (KG) as side information in recommendation system to address cold start and sparsity issue and improve the recommendation performance. Existing KG-aware recommendation model use the feature of neighboring entities and structural information to update the embedding of currently located entity. Although the fruitful information is beneficial to the following task, the cost of exploring the entire graph is massive and impractical. In order to reduce the computational cost and maintain the pattern of extracting features, KG-aware recommendation model usually utilize fixed-size and random set of neighbors rather than complete information in KG. Nonetheless, there are two critical issues in these approaches: First of all, fixed-size and randomly selected neighbors restrict the view of graph. In addition, as the order of graph feature increases, the growth of parameter dimensionality of the model may lead the training process hard to converge. To solve the aforementioned limitations, we propose GraphSW, a strategy based on stage-wise training framework which would only access to a subset of the entities in KG in every stage. During the following stages, the learned embedding from previous stages is provided to the network in the next stage and the model can learn the information gradually from the KG. We apply stage-wise training on two SOTA recommendation models, RippleNet and Knowledge Graph Convolutional Networks (KGCN). Moreover, we evaluate the performance on six real world datasets, Last.FM 2011, Book-Crossing,movie, LFM-1b 2015, Amazon-book and Yelp 2018. The result of our experiments shows that proposed strategy can help both models to collect more information from the KG and improve the performance. Furthermore, it is observed that GraphSW can assist KGCN to converge effectively in high-order graph feature.

Includes members:
  Member "unifying knowledge graph learning and recommendation towards a better understanding of user preferences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "neural graph collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to hash with graph neural networks for recommender systems")
  Member "knowledge graph convolutional networks for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "graph convolutional neural networks for web scale recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "ripplenet propagating user preferences on the knowledge graph for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "explainable reasoning over knowledge graphs for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "kb4rec a dataset for linking knowledge bases with recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforced negative sampling over knowledge graph for recommendation")
  Member "densely connected pyramid dehazing network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gated fusion network for degraded image super resolution")

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)