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: "understanding and optimizing the performance of distributed machine learning applications on apache spark"
  Abstract: In this paper we explore the performance limits of Apache Spark for machine learning applications. We begin by analyzing the characteristics of a state-of-the-art distributed machine learning algorithm implemented in Spark and compare it to an equivalent reference implementation using the high performance computing framework MPI. We identify critical bottlenecks of the Spark framework and carefully study their implications on the performance of the algorithm. In order to improve Spark performance we then propose a number of practical techniques to alleviate some of its overheads. However, optimizing computational efficiency and framework related overheads is not the only key to performance — we demonstrate that in order to get the best performance out of any implementation it is necessary to carefully tune the algorithm to the respective trade-off between computation time and communication latency. The optimal trade-off depends on both the properties of the distributed algorithm as well as infrastructure and framework-related characteristics. Finally, we apply these technical and algorithmic optimizations to three different distributed linear machine learning algorithms that have been implemented in Spark. We present results using five large datasets and demonstrate that by using the proposed optimizations, we can achieve a reduction in the performance difference between Spark and MPI from 20x to 2x.

Connected through hyperedge HE-1 (induced by source paper "addressing algorithmic bottlenecks in elastic machine learning with chicle"):
  with members:
    - "elastic cocoa scaling in to improve convergence"
    - "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"
    - "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 "performance analysis and comparison of distributed machine learning systems"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "revisiting distributed synchronous sgd"
    - "performance modeling of distributed deep neural networks"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "large scale stochastic learning using gpus"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "adding vs averaging in distributed primal dual optimization"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "distributed mini batch sdca"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"

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.DC(Distributed, Parallel, and Cluster Computing)