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 "decentralized gradient methods does topology matter"):
  Abstract: Consensus-based distributed optimization methods have recently been advocated as alternatives to parameter server and ring all-reduce paradigms for large scale training of machine learning models. In this case, each worker maintains a local estimate of the optimal parameter vector and iteratively updates it by averaging the estimates obtained from its neighbors, and applying a correction on the basis of its local dataset. While theoretical results suggest that worker communication topology should have strong impact on the number of epochs needed to converge, previous experiments have shown the opposite conclusion. This paper sheds lights on this apparent contradiction and show how sparse topologies can lead to faster convergence even in the absence of communication delays.

Includes members:
  Member "hop heterogeneity aware decentralized training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "heterogeneity aware asynchronous decentralized training")
  Member "stochastic gradient push for distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "near optimal straggler mitigation for distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a locality based approach for coded computation")
  Member "flexible scheduling of distributed analytic applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a data driven approach to dynamically adjust resource alocation for compute clusters")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "federated optimization distributed optimization beyond the datacenter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ternary compression for communication efficient federated learning")
  Member "cyclical learning rates for training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)