human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the dimensionality of embeddings for sparse features and data"
  Abstract: In this note we discuss a common misconception, namely that embeddings are always used to reduce the dimensionality of the item space. We show that when we measure dimensionality in terms of information entropy then the embedding of sparse probability distributions, that can be used to represent sparse features or data, may or not reduce the dimensionality of the item space. However, the embeddings do provide a different and often more meaningful representation of the items for a particular task at hand. Also, we give upper bounds and more precise guidelines for choosing the embedding dimension.

Connected through hyperedge HE-1 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "saec similarity aware embedding compression in recommendation systems"
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "tensorized embedding layers for efficient model compression"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"
    - "on the difficulty of evaluating baselines a study on recommender systems"

Connected through hyperedge HE-2 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "mlperf training benchmark"
    - "recnmp accelerating personalized recommendation with near memory processing"
    - "bandana using non volatile memory for storing deep learning models"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"

Connected through hyperedge HE-3 (induced by source paper "compositional embeddings using complementary partitions for memory efficient recommendation systems"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "tensorized embedding layers for efficient model compression"
    - "on the convergence of adam and beyond"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "mars memory attention aware recommender system"
    - "on the dimensionality of word embedding"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "deep learning recommendation model for personalization and recommendation systems"):
  with members:
    - "towards federated learning at scale system design"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "neural collaborative filtering"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "tensor methods and recommender systems"

Question: Please tell me which class the center node 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)