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 "distilling structured knowledge into embeddings for explainable and accurate recommendation"):
  Abstract: Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and flexibility. However, they also have such intrinsic limitations as lacking explainability and suffering from data sparsity. In this paper, we propose an end-to-end joint learning framework to get around these limitations without introducing any extra overhead by distilling structured knowledge from a differentiable path-based recommendation model. Through extensive experiments, we show that our proposed framework can achieve state-of-the-art recommendation performance and meanwhile provide interpretable recommendation reasons.

Includes members:
  Member "neural ir meets graph embedding a ranking model for product search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "document network embedding coping for missing content and missing links")
  Member "jointly learning explainable rules for recommendation with knowledge graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "deep learning based recommender system a survey and new perspectives":
    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 "ripplenet propagating user preferences on the knowledge graph for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "explainable recommendation a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "neural factorization machines for sparse predictive analytics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")
  Member "deep mutual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "advances in collaborative filtering and ranking")

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)