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 "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"):
  Abstract: Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow level of entity raw data or embeddings. This may lead to suboptimal performance, since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system.    In this paper, we jointly learn the model of recommendation and knowledge graph completion. Distinct from previous KG-based recommendation methods, we transfer the relation information in KG, so as to understand the reasons that a user likes an item. As an example, if a user has watched several movies directed by (relation) the same person (entity), we can infer that the director relation plays a critical role when the user makes the decision, thus help to understand the user's preference at a finer granularity.    Technically, we contribute a new translation-based recommendation model, which specially accounts for various preferences in translating a user to an item, and then jointly train it with a KG completion model by combining several transfer schemes. Extensive experiments on two benchmark datasets show that our method outperforms state-of-the-art KG-based recommendation methods. Further analysis verifies the positive effect of joint training on both tasks of recommendation and KG completion, and the advantage of our model in understanding user preference. We publish our project at https://github.com/TaoMiner/joint-kg-recommender.

Includes members:
  Member "temporal relational ranking for stock prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "neural collective entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "kgat knowledge graph attention network for recommendation")
  Member "latent relational metric learning via memory based attention for collaborative ranking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "learning over knowledge base embeddings for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "joint representation learning of cross lingual words and entities via attentive distant supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "is aligning embedding spaces a challenging task an analysis of the existing methods")
  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 "adversarial personalized ranking for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "explicit state tracking with semi supervisionfor neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "estimation action reflection towards deep interaction between conversational and 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)