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 "learning heterogeneous knowledge base embeddings for explainable recommendation"):
  Abstract: Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-based approaches with shallow or deep models - usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the research focus on CF approaches. However, structured knowledge exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.

Includes members:
  Member "explainable recommendation a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "translation based recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in collaborative filtering and ranking")
  Member "joint deep modeling of users and items using reviews for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "explainable entity based recommendations with knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "max margin nonparametric latent feature models for link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi stage self supervised learning for graph convolutional networks on graphs with few labels")
  Member "vbpr visual bayesian personalized ranking from implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective")
  Member "image based recommendations on styles and substitutes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "distributed representations of sentences and documents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)