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 "fast and accurate knowledge aware document representation enhancement for news recommendations"):
  Abstract: Knowledge graph contains well-structured external information and has shown to be useful for recommender systems. Most existing knowledge-aware methods assume that the item from recommender systems can be linked to an entity in a knowledge graph, thus item embeddings can be better learned by jointly modeling of both recommender systems and a knowledge graph. However, this is not the situation for news recommendation, where items, namely news articles, are in fact related to a collection of knowledge entities. The importance score and semantic information of entities in one article differ from each other, which depend on the topic of the article and relations among co-occurred entities. How to fully utilize these entities for better news recommendation service is non-trivial. #R##N#In this paper, we propose a fast and effective knowledge-aware representation enhancement model for improving news document understanding. The model, named \emph{KRED}, consists of three layers: (1) an entity representation layer; (2) a context embedding layer; and (3) an information distillation layer. An entity is represented by the embeddings of itself and its surrounding entities. The context embedding layer is designed to distinguish dynamic context of different entities such as frequency, category and position. The information distillation layer will aggregate the entity embeddings under the guidance of the original document vector, transforming the document vector into a new one. We have conduct extensive experiments on a real-world news reading dataset. The results demonstrate that our proposed model greatly benefits a variety of news recommendation tasks, including personalized news recommendation, article category classification, article popularity prediction and local news detection.

Includes members:
  Member "multi task feature learning for knowledge graph enhanced recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "entity context and relational paths for knowledge graph completion")
  Member "kgat knowledge graph attention network for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "unifying knowledge graph learning and recommendation towards a better understanding of user preferences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "knowledge aware graph neural networks with label smoothness regularization for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "knowledge graph convolutional networks for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "npa neural news recommendation with personalized attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural news recommendation with attentive multi view learning")
  Member "graph convolutional neural networks for web scale recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "a survey on knowledge graph based recommender systems")

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)