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 "mixed dimension embeddings with application to memory efficient recommendation systems"):
  Abstract: In many real-world applications, e.g. recommendation systems, certain items appear much more frequently than other items. However, standard embedding methods---which form the basis of many ML algorithms---allocate the same dimension to all of the items. This leads to statistical and memory inefficiencies. In this work, we propose mixed dimension embedding layers in which the dimension of a particular embedding vector can depend on the frequency of the item. This approach drastically reduces the memory requirement for the embedding, while maintaining and sometimes improving the ML performance. We show that the proposed mixed dimension layers achieve a higher accuracy, while using 8X fewer parameters, for collaborative filtering on the MovieLens dataset. Also, they improve accuracy by 0.1% using half as many parameters or maintain baseline accuracy using 16X fewer parameters for click-through rate prediction task on the Criteo Kaggle dataset.

Includes members:
  Member "on the dimensionality of embeddings for sparse features and data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation")
  Member "deep learning recommendation model for personalization and recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "megatron lm training multi billion parameter language models using model parallelism":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "saec similarity aware embedding compression in recommendation systems":
    also connected through other hyperedges:
      (none)
  Member "practice on long sequential user behavior modeling for click through rate prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint optimization of tree based index and deep model for recommender systems")
  Member "tensorized embedding layers for efficient model compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a tensorized transformer for language modeling")
  Member "hierarchy based image embeddings for semantic image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "block based singular value decomposition approach to matrix factorization for recommender systems":
    also connected through other hyperedges:
      (none)

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.LG(Machine Learning)