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 parallel sgd method with strong convergence"
  Abstract: This paper proposes a novel parallel stochastic gradient descent (SGD) method that is obtained by applying parallel sets of SGD iterations (each set operating on one node using the data residing in it) for finding the direction in each iteration of a batch descent method. The method has strong convergence properties. Experiments on datasets with high dimensional feature spaces show the value of this method.

Connected through hyperedge HE-1 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"

Connected through hyperedge HE-2 (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"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"):
  with members:
    - "introduction to online convex optimization"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "distributed learning communication complexity and privacy"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "better mini batch algorithms via accelerated gradient methods"

Connected through hyperedge HE-4 (induced by source paper "communication complexity of distributed convex learning and optimization"):
  with members:
    - "communication efficient distributed dual coordinate ascent"
    - "improved distributed principal component analysis"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "distributed learning communication complexity and privacy"
    - "better mini batch algorithms via accelerated gradient methods"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-5 (induced by source paper "communication efficient distributed optimization using an approximate newton type method"):
  with members:
    - "better mini batch algorithms via accelerated gradient methods"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "a reliable effective terascale linear learning system"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-6 (induced by source paper "an efficient distributed learning algorithm based on effective local functional approximations"):
  with members:
    - "communication efficient distributed dual coordinate ascent"
    - "analysis of distributed stochastic dual coordinate ascent"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "a reliable effective terascale linear learning system"

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)