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 "explainable reasoning over knowledge graphs for recommendation"):
  Abstract: Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.

Includes members:
  Member "ripplenet propagating user preferences on the knowledge graph for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "adversarial ranking attack and defense")
  Member "neural factorization machines for sparse predictive analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")
  Member "item silk road recommending items from information domains to social users":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "auto encoding user ratings via knowledge graphs in recommendation scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an explainable autoencoder for collaborative filtering recommendation")
  Member "chains of reasoning over entities relations and text using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings")
  Member "a generic coordinate descent framework for learning from implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "compositional vector space models for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vqa lol visual question answering under the lens of logic")

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)