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 "modeling embedding dimension correlations via convolutional neural collaborative filtering"):
  Abstract: As the core of recommender system, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, and so on. Benefited from the strong representation power, neural networks have recently revolutionized the recommendation research, setting up a new standard for CF. However, existing neural recommender models do not explicitly consider the correlations among embedding dimensions, making them less effective in modeling the interaction function between users and items. In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF. We propose a novel and general neural collaborative filtering framework, namely ConvNCF, which is featured with two designs: 1) applying outer product on user embedding and item embedding to explicitly model the pairwise correlations between embedding dimensions, and 2) employing convolutional neural network above the outer product to learn the high-order correlations among embedding dimensions. To justify our proposal, we present three instantiations of ConvNCF by using different inputs to represent a user and conduct experiments on two real-world datasets. Extensive results verify the utility of modeling embedding dimension correlations with ConvNCF, which outperforms several competitive CF methods.

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 "mmalfm explainable recommendation by leveraging reviews and images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "fast matrix factorization with non uniform weights on missing data":
    also connected through other hyperedges:
      (none)
  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 "product based neural networks for user response prediction over multi field categorical data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi graph convolution collaborative filtering")
  Member "outer product based neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")
  Member "adversarial personalized ranking for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "a simple convolutional generative network for next item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")

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)