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: "l1 regularized distributed optimization a communication efficient primal dual framework"
  Abstract: Reference EPFL-REPORT-229241 URL: http://arxiv.org/abs/1512.04011 Record created on 2017-06-21, modified on 2017-07-12

Connected through hyperedge HE-1 (induced by source paper "stochastic distributed and federated optimization for machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "randomized distributed mean estimation accuracy vs communication"
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "practical secure aggregation for federated learning on user held data"

Connected through hyperedge HE-2 (induced by source paper "large scale stochastic learning using gpus"):
  with members:
    - "understanding and optimizing the performance of distributed machine learning applications on apache spark"
    - "revisiting distributed synchronous sgd"
    - "adding vs averaging in distributed primal dual optimization"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "distributed mini batch sdca"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"

Connected through hyperedge HE-3 (induced by source paper "federated optimization distributed machine learning for on device intelligence"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "distributed optimization with arbitrary local solvers"
    - "distributed mini batch sdca"

Connected through hyperedge HE-4 (induced by source paper "primal dual rates and certificates"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
    - "distributed optimization with arbitrary local solvers"
    - "sdca without duality"
    - "distributed mini batch sdca"
    - "mini batch primal and dual methods for svms"
    - "an equivalence between the lasso and support vector machines"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-5 (induced by source paper "distributed optimization with arbitrary local solvers"):
  with members:
    - "importance sampling for minibatches"
    - "primal dual rates and certificates"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "distributed inexact damped newton method data partitioning and load balancing"
    - "adding vs averaging in distributed primal dual optimization"
    - "federated optimization distributed optimization beyond the datacenter"
    - "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
    - "sdna stochastic dual newton ascent for empirical risk 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)