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 "distributed equivalent substitution training for large scale recommender systems"):
  Abstract: We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for recommender systems with large-scale dynamic sparse features. Our framework achieves faster convergence with less communication overhead and better computing resource utilization. DES strategy splits a weights-rich operator into sub-operators with co-located weights and aggregates partial results with much smaller communication cost to form a computationally equivalent substitution to the original operator. We show that for different types of models that recommender systems use, we can always find computational equivalent substitutions and splitting strategies for their weights-rich operators with theoretical communication load reduced ranging from 72.26% to 99.77%. We also present an implementation of DES that outperforms state-of-the-art recommender systems. Experiments show that our framework achieves up to 83% communication savings compared to other recommender systems, and can bring up to 4.5x improvement on throughput for deep models.

Includes members:
  Member "large batch optimization for deep learning training bert in 76 minutes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "xdeepfm combining explicit and implicit feature interactions for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "highly scalable deep learning training system with mixed precision training imagenet in four minutes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "exploring hidden dimensions in parallelizing convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices")
  Member "integrated model batch and domain parallelism in training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "flexible communication avoiding matrix multiplication on fpga with high level synthesis")
  Member "device placement optimization with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon adaptive code optimization for expedited deep neural network compilation")
  Member "revisiting distributed synchronous sgd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "deepfm a factorization machine based neural network for ctr prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)