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: "elastic cocoa scaling in to improve convergence"
  Abstract: In this paper we experimentally analyze the convergence behavior of CoCoA and show, that the number of workers required to achieve the highest convergence rate at any point in time, changes over the course of the training. Based on this observation, we build Chicle, an elastic framework that dynamically adjusts the number of workers based on feedback from the training algorithm, in order to select the number of workers that results in the highest convergence rate. In our evaluation of 6 datasets, we show that Chicle is able to accelerate the time-to-accuracy by a factor of up to 5.96x compared to the best static setting, while being robust enough to find an optimal or near-optimal setting automatically in most cases.

Connected through hyperedge HE-1 (induced by source paper "addressing algorithmic bottlenecks in elastic machine learning with chicle"):
  with members:
    - "snap ml a hierarchical framework for machine learning"
    - "don t use large mini batches use local sgd"
    - "slaq quality driven scheduling for distributed machine learning"
    - "measuring the effects of data parallelism on neural network training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "understanding and optimizing the performance of distributed machine learning applications on apache spark"
    - "don t decay the learning rate increase the batch size"
    - "large batch training of convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "syscd a system aware parallel coordinate descent algorithm"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "snap ml a hierarchical framework for machine learning"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "dimmwitted a study of main memory statistical analytics"
    - "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)