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 fixed point methods with compressed iterates"):
  Abstract: We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed. This problem is motivated by the practice of federated learning, where a large model stored in the cloud is compressed before it is sent to a mobile device, which then proceeds with training based on local data. We develop standard and variance reduced methods, and establish communication complexity bounds. Our algorithms are the first distributed methods with compressed iterates, and the first fixed point methods with compressed iterates.

Includes members:
  Member "distributed learning with compressed gradient differences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "decentralized stochastic optimization and gossip algorithms with compressed communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "sgd general analysis and improved rates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated learning of a mixture of global and local models")
  Member "natural compression for distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts")
  Member "fedpaq a communication efficient federated learning method with periodic averaging and quantization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "personalized federated learning a meta learning approach")
  Member "gradient descent with compressed iterates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix sketching for secure collaborative machine learning")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "quantized decentralized consensus optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "decentralized deep learning with arbitrary communication 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)