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 "a general distributed dual coordinate optimization framework for regularized loss minimization"):
  Abstract: In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines. It is customary to employ the "data parallelism" approach, where the aggregated training loss is minimized without moving data across machines. In this paper, we introduce a novel distributed dual formulation for regularized loss minimization problems that can directly handle data parallelism under the distributed computing environment. This formulation allows us to systematically derive dual coordinate optimization procedures, which we refer to as Distributed Alternating Dual Maximization (DADM). The method extends earlier studies about distributed SDCA algorithms and has a rigorous theoretical analysis. Based on the new formulation, we also develop an accelerated DADM algorithm by generalizing the acceleration technique from the accelerated SDCA algorithm to the distributed setting. Our empirical studies show that our novel approach significantly improves the previous state-of-the-art distributed dual coordinate optimization algorithms.

Includes members:
  Member "cocoa a general framework for communication efficient distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "adding vs averaging in distributed primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "distributed optimization with arbitrary local solvers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "distributed mini batch sdca":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic distributed and federated optimization for machine learning")
  Member "a distributed block coordinate descent method for training l_1 regularized linear classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a distributed second order algorithm you can trust")
  Member "saga a fast incremental gradient method with support for non strongly convex composite objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "analysis of distributed stochastic dual coordinate ascent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "central server free federated learning over single sided trust social networks")
  Member "communication efficient distributed optimization using an approximate newton type method":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaffold stochastic controlled averaging for 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)