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 "gadmm fast and communication efficient framework for distributed machine learning"):
  Abstract: When the data is distributed across multiple servers, efficient data exchange between the servers (or workers) for solving the distributed learning problem is an important problem and is the focus of this paper. We propose a fast, privacy-aware, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm, GADMM, is based on Alternating Direct Method of Multiplier (ADMM) algorithm. The key novelty in GADMM is that each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with lower amount of communication in each exchange. We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it is faster and more communication-efficient than the state-of-the-art communication-efficient centralized algorithms such as the Lazily Aggregated Gradient (LAG), in linear and logistic regression tasks on synthetic and real datasets. Furthermore, we propose Dynamic GADMM (D-GADMM), a variant of GADMM, and prove its convergence under time-varying network topology of the workers.

Includes members:
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "adaptive federated learning in resource constrained edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "communication efficient on device machine learning federated distillation and augmentation under non iid private data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "proximity without consensus in online multiagent optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "q gadmm quantized group admm for communication efficient decentralized machine learning")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "distributed mean estimation with limited communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rpn a residual pooling network for efficient federated learning")
  Member "distributed optimization with arbitrary local solvers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "multi agent distributed optimization via inexact consensus admm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")

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)