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 "relational collaborative filtering modeling multiple item relations for recommendation"):
  Abstract: Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations.

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 "recommendation through mixtures of heterogeneous item relationships":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequential recommender systems challenges progress and prospects")
  Member "latent relational metric learning via memory based attention for collaborative ranking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "learning heterogeneous knowledge base embeddings for explainable recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "ripplenet propagating user preferences on the knowledge graph for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "adversarial personalized ranking for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)