human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "limits on the universal method for matrix multiplication"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"

Connected through hyperedge HE-2 (induced by source paper "the scalability for parallel machine learning training algorithm dataset matters"):
  with members:
    - "decentralization meets quantization"
    - "exascale deep learning for climate analytics"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "distributed mean estimation with limited communication"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed optimization with arbitrary local solvers"
    - "fast algorithms for convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "a distributed quasi newton algorithm for primal and dual regularized empirical risk minimization"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "using neural networks to detect line outages from pmu data"
    - "proximal quasi newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates"
    - "mllib machine learning in apache spark"
    - "practical inexact proximal quasi newton method with global complexity analysis"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "duality between subgradient and conditional gradient methods"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-4 (induced by source paper "a distributed second order algorithm you can trust"):
  with members:
    - "giant globally improved approximate newton method for distributed optimization"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-5 (induced by source paper "giant globally improved approximate newton method for distributed optimization"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed machine learning for on device intelligence"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "mllib machine learning in apache spark"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-6 (induced by source paper "l1 regularized distributed optimization a communication efficient primal dual framework"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "mllib machine learning in apache spark"
    - "an equivalence between the lasso and support vector machines"
    - "parallel coordinate descent for l1 regularized loss minimization"

Question: Please tell me which class the center node 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)