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 "compositional embeddings using complementary partitions for memory efficient recommendation systems"):
  Abstract: Modern deep learning-based recommendation systems exploit hundreds to thousands of different categorical features, each with millions of different categories ranging from clicks to posts. To respect the natural diversity within the categorical data, embeddings map each category to a unique dense representation within an embedded space. Since each categorical feature could take on as many as tens of millions of different possible categories, the embedding tables form the primary memory bottleneck during both training and inference. We propose a novel approach for reducing the embedding size in an end-to-end fashion by exploiting complementary partitions of the category set to produce a unique embedding vector for each category without explicit definition. By storing multiple smaller embedding tables based on each complementary partition and combining embeddings from each table, we define a unique embedding for each category at smaller cost. This approach may be interpreted as using a specific fixed codebook to ensure uniqueness of each category's representation. Our experimental results demonstrate the effectiveness of our approach over the hashing trick for reducing the size of the embedding tables in terms of model loss and accuracy, while retaining a similar reduction in the number of parameters.

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 "mixed dimension embeddings with application to memory efficient recommendation systems")
  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 "tensorized embedding layers for efficient model compression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "xdeepfm combining explicit and implicit feature interactions for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "mars memory attention aware recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an introductory survey on attention mechanisms in nlp problems")
  Member "on the dimensionality of word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "atrank an attention based user behavior modeling framework for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attentive item2vec neural attentive user representations")

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)