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 distributed second order algorithm you can trust"
  Abstract: Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potentially require fewer communication rounds to converge. However, there are significant drawbacks that impede their wide adoption, such as the computation and the communication of a large Hessian matrix. In this paper we present a new algorithm for distributed training of generalized linear models that only requires the computation of diagonal blocks of the Hessian matrix on the individual workers. To deal with this approximate information we propose an adaptive approach that - akin to trust-region methods - dynamically adapts the auxiliary model to compensate for modeling errors. We provide theoretical rates of convergence for a wide class of problems including L1-regularized objectives. We also demonstrate that our approach achieves state-of-the-art results on multiple large benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "oversketched newton fast convex optimization for serverless systems"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "harnessing the power of serverless runtimes for large scale optimization"
    - "serverless computing one step forward two steps back"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "oversketch approximate matrix multiplication for the cloud"
    - "giant globally improved approximate newton method for distributed optimization"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "probabilistic synchronous parallel"
    - "revisiting distributed synchronous sgd"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "web scale topic models in spark an asynchronous parameter server"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-3 (induced by source paper "a distributed quasi newton algorithm for primal and dual regularized empirical risk minimization"):
  with members:
    - "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"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "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 "ellipsoidal trust region methods for neural network training"):
  with members:
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "failures of gradient based deep learning"
    - "sub sampled cubic regularization for non convex optimization"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-5 (induced by source paper "syscd a system aware parallel coordinate descent algorithm"):
  with members:
    - "elastic cocoa scaling in to improve convergence"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "cola decentralized linear learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"
    - "sparsified sgd with memory"
    - "d 2 decentralized training over decentralized data"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "primal dual rates and certificates"

Connected through hyperedge HE-7 (induced by source paper "newton admm a distributed gpu accelerated optimizer for multiclass classification problems"):
  with members:
    - "giant globally improved approximate newton method for distributed optimization"
    - "revisiting distributed synchronous sgd"
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "adaptive consensus admm for distributed optimization"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "dynanewton accelerating newton s method for machine learning"
    - "how to scale distributed deep learning"
    - "adaptive admm with spectral penalty parameter selection"

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)