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 "latent relational metric learning via memory based attention for collaborative ranking"):
  Abstract: This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (Latent Relational Metric Learning) is a novel extension of metric learning approaches for recommendation. More specifically, instead of simple push pull mechanisms between user and item pairs, we propose to learn latent relations for each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learning approaches. This not only enables better performance but also a greater extent of modeling capability, allowing our model to scale to larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture not only demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by 6%-7.5% in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.

Includes members:
  Member "deep learning based recommender system a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "multi pointer co attention networks for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "personalized top n sequential recommendation via convolutional sequence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advances in collaborative filtering and ranking")
  Member "a compare propagate architecture with alignment factorization for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discourse marker augmented network with reinforcement learning for natural language inference")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in collaborative filtering and ranking")
  Member "translation based recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in collaborative filtering and ranking")
  Member "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")
  Member "fast matrix factorization for online recommendation with implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to hash with graph neural networks for 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.AI(Artificial Intelligence)